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Speaker 1: From UFOs to psychic powers and government conspiracies. History is

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Speaker 1: riddled with unexplained events. You can turn back now or

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Speaker 1: learn this stuff they don't want you to know. A

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Speaker 1: production of iHeartRadio.

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Speaker 2: Hello, welcome back to the show. My name is Matt,

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Speaker 2: my name is Noah.

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Speaker 3: They call me Ben. We're joined as always with our

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Speaker 3: super producer, Dylan the Tennessee pal Fagan. Most importantly, you

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Speaker 3: are you. You are here that makes this the stuff

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Speaker 3: they don't want you to know. And tonight, friends and neighbors,

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Speaker 3: fellow conspiracy realist, and hopefully literally everyone on Netflix. We

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Speaker 3: are exploring something a lot of us honestly don't like

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Speaker 3: thinking about. Credit. Jay Z has a whole song about it,

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Speaker 3: which is pretty good. We're talking about a song called

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Speaker 3: the Story of OJ by jay Z, controversial to some

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Speaker 3: talks about credit. We also want to let you know, folks,

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Speaker 3: since we're back to our original roots as a video podcast,

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Speaker 3: we've got to let you know that some of our

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Speaker 3: scenery is changing back and forth as we are on

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Speaker 3: the road at various times. And we can't thank you

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Speaker 3: enough for sticking with us, and we're giving you a

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Speaker 3: lot of credit for this. Guys. It's true that for

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Speaker 3: a lot of our longtime listeners, friends and neighbors around

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Speaker 3: the world, the idea of credit or financial stuff may

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Speaker 3: not seem to be the usual stuff they don't want

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Speaker 3: you to know fair, but we argue credit is inherently

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Speaker 3: conspiratorial about it. You guys, we talked about it a

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Speaker 3: little bit off air before we were recording today here

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Speaker 3: in the United States. Your financial trustworthiness what they call

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Speaker 3: your credit worthiness, your like how much society likes you,

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Speaker 3: is based on something called a credit score, which later

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Speaker 3: informs our earlier episode on the government of China's sesame

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Speaker 3: credit or social credits.

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Speaker 4: Just a further like gamification of the whole system that

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Speaker 4: applies to literally everything, like out of that Black Mirror

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Speaker 4: episode with Ron Howard's daughter, where they've got a whole

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Speaker 4: system in place where you literally can't even get a

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Speaker 4: plane ticket, or you can't there's certain things that you

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Speaker 4: were just excluded from. If your you know, your clout

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Speaker 4: number sinks below a certain threshold.

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Speaker 5: That's literally what all this stuff is.

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Speaker 3: Oh, one hundred percent, a million percent.

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Speaker 2: It goes back to the the latest eighteen hundred you

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Speaker 2: can get eight ninety nine low company called retail credit company.

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Speaker 2: But it goes back to organizations that want to know

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Speaker 2: if you, as a company or corporation are good for.

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Speaker 3: It, are solvent?

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Speaker 2: Yeah, and then it turns into are you the individual

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Speaker 2: good for it when it comes to being loaned money

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Speaker 2: and given a chance to pay interest on something, which

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Speaker 2: is just extortion.

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Speaker 3: I love that we're walking down the street already for extortion,

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Speaker 3: which I argue is true and usurious. That's our word

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Speaker 3: of the day, right, and we.

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Speaker 4: Get hopscotch down the street just a little further to

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Speaker 4: talk about the disconnect between the way people like you

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Speaker 4: or I are treated and the way the one percent

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Speaker 4: of the one percent and corporations are treated.

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Speaker 6: Where you know, debt is a good thing.

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Speaker 3: Yeah, An average person in the United States is a

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Speaker 3: very US centric episode, folks. Average person in the United

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Speaker 3: States owe somebody something that is that person's problem. Average

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Speaker 3: one percenter And sorry, guys, you can be a one

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Speaker 3: percenter and still an average schmuck if you owe something

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Speaker 3: that's the institution's problem. And that is time and time

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Speaker 3: again every day millions of people rely on this concept.

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Speaker 3: We introduce just now a credit score to finance loans,

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Speaker 3: credit cards, houses, cars, way much more and this system

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Speaker 3: is so bizarre it doesn't make sense. Everybody accepts it

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Speaker 3: as normal, the way people walk around with neck ties,

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Speaker 3: with ties with cravats, like why why are you doing this?

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Speaker 2: Why not? When it goes back to how much information

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Speaker 2: can they gather on you, whether you're a company or

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Speaker 2: an individual, it's literally what it is, how much info

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Speaker 2: can they get on you? And as that's the stuff

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Speaker 2: they don't want you to know at the heart of

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Speaker 2: this thing. And as we get into it, we're going

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Speaker 2: to find that a lot of the things that we've

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Speaker 2: been worried about when it comes to our privacy, the

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Speaker 2: thing ben as you say, does not exist anymore. As

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Speaker 2: we get closer to those things we've been so worried about,

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Speaker 2: we kind of forgot about. I was gonna say trade,

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Speaker 2: but the Equifax and experience and transcendion.

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Speaker 3: Ah, yes, a bit of a spoiler. Let's call it foreshadowing.

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Speaker 3: Let's talk about the as we're introducing the big bag

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Speaker 3: of badgers here, the big three badgers in the bag.

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Speaker 3: The lets talk about how weird it is to explain

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Speaker 3: this so again, this system is so bizarre. If you

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Speaker 3: explained it to an outsider, your credit goes down if

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Speaker 3: you get in too much debt. It can go up

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Speaker 3: when you get more credit cards, more opportunity for debt.

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Speaker 3: It can go up if you have a long term

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Speaker 3: debt and you make successful payments, but it can go

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Speaker 3: down if you close a credit card. It can go

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Speaker 3: down if you actually pay off your debt. It's confusing

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Speaker 3: as heck. We argue that's at least partially by design.

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Speaker 3: And get this, folks, there is no single government agency

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Speaker 3: determining this. Instead, there is a cabal of private groups

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Speaker 3: known as credit bureaus. I vote we talk.

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Speaker 4: About oh Man bureaus already has a shadowy ring to it.

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Speaker 4: We're gonna talk about all of those aspects of what

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Speaker 4: it means to have a credit score.

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Speaker 5: After a quick little pause.

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Speaker 3: Here or the facts, all right, we got to start

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Speaker 3: with credit cards, most physical manifestation of debt in the

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Speaker 3: entirety of the United States. If we look at the

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Speaker 3: New York FED, as of twenty twenty five, collective credit

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Speaker 3: card debt in the United States has hit over one

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Speaker 3: point two three trillion dollars. Again, that's credit cards alone.

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Speaker 3: That's not counting student loans, medical debt. The main the

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Speaker 3: leader in bankruptcy and.

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Speaker 4: The lower your score is obviously the higher the interest

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Speaker 4: rate you're going to be paying.

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Speaker 3: Yeah, and one point two three brillion dollars is that

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Speaker 3: even real money? It's approximately and this is again per

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Speaker 3: the FED, that is something like six thousand, five hundred

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Speaker 3: to six thousand, five hundred and twenty three bucks per person.

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Speaker 3: It's also a creepy, crooked average because it's not evenly distributed.

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Speaker 3: If you're in a younger generation like millennials or lower

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Speaker 3: income households, then you statistically carry more of that debt.

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Speaker 3: And this is the thing we love because we're pro nerd.

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Speaker 3: On our show, multiple economists I almost call them economicist

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Speaker 3: multiple economists argue this is bad, but their concerns get

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Speaker 3: dismissed so often because sometimes those nerdy proclamations are politically

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Speaker 3: or economically inconvenient, you know, like now, as we record

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Speaker 3: on Tuesday, January twentieth, Well.

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Speaker 2: It's a weird situation that the credit card companies are

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Speaker 2: in when there are so many people using their products

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Speaker 2: that are in such massive amounts of debt. Because, as

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Speaker 2: we said, that's.

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Speaker 4: The idea of credit card as a product. By the way,

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Speaker 4: it's that's what I mean. I mean, it is, and

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Speaker 4: that's how they're selling it, but it still just kind

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Speaker 4: of boggles my mind a little bit when you think

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Speaker 4: about it.

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Speaker 2: And each company has dozens of them different types and

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Speaker 2: styles of product that they can push on. You remember

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Speaker 2: we talked about the Wells Fargo episode. We talked about

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Speaker 2: them pushing different products. So when they one of the

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Speaker 2: reasons they had to create all those fake accounts that

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Speaker 2: one time that happened, yes, by one time, was because

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Speaker 2: they had to sell more products in the form of

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Speaker 2: accounts and cards and those kinds of things. The point

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Speaker 2: about this that's so weird is that those companies are

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Speaker 2: going to still be getting interest payments. Theoretically, that's a

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Speaker 2: common tactic that humans have decided to do. We put

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Speaker 2: a bunch of money on those credit cards, and then

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Speaker 2: we'll just pay the interest and it'll, you know, continue

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Speaker 2: to sit there, that big debt, but we'll pay the

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Speaker 2: interest and maybe a tiny bit of that that loan

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Speaker 2: that we've taken out essentially through our credit because it

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Speaker 2: is a loan. A credit card is alone and every

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Speaker 2: time you use it, right.

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Speaker 3: Sure, like the industrial age.

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Speaker 2: But those credit card companies are getting those interest payments,

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Speaker 2: so they're theoretically fine as long as it doesn't have

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Speaker 2: a big sweeping you know, black Tuesday. Was it a

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Speaker 2: Black Tuesday that happened where everybody was like just decided

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Speaker 2: to stop using some of your cancel everything, or need

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Speaker 2: everything all at once.

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Speaker 3: Or run on a bank, right like in the Great

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Speaker 3: Depression days. And I'm going to go on record again saying,

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Speaker 3: just as a writer, that is a terrible name. There

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Speaker 3: was nothing great about it. It was just depressing and nuts.

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Speaker 3: We also we go back to credit cards, right, that's

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Speaker 3: our entry point here because we think about them as

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Speaker 3: a pretty modern financial innovation, but the concept of credit

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Speaker 3: in general dates back into antiquity. Like our good friends

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Speaker 3: at IGU point out, as soon as somebody came up

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Speaker 3: with the idea of cash, a second person came up

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Speaker 3: and said, nol, what's that line from Popeye we always

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Speaker 3: like about hamburgers.

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Speaker 6: I'll gladly pay you tuesday for a hamburger today.

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Speaker 3: Right. Yeah. The concept of credit, or.

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Speaker 4: The concept of not even cash, just value, right, Like

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Speaker 4: I mean, just stuff holding value and being worth more

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Speaker 4: than the physical object and its utility. That is the

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Speaker 4: crux of all of this, I guess, right, Yeah, So.

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Speaker 3: Our third guy comes up and walks into this conversation

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Speaker 3: and says, hey, I got a pitch ed. The proposition was,

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Speaker 3: why don't we replace currency, right or goods with concepts,

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Speaker 3: the idea of owing someone something without money immediately changing hands,

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Speaker 3: and we don't. We've talked about this in previous episodes,

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Speaker 3: but there's a there's a great outfit called the Points

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Speaker 3: Guy that sums up the history pretty darn well talking

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Speaker 3: about how this this concept of credit and credit later

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Speaker 3: credit cards and then later credit scores and then later

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Speaker 3: credit bureaus sort of the antagonist of our story tonight.

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Speaker 3: It dates back to Mesopotamian and Haroping civilizations. They they

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Speaker 3: were using clay tablets right because they didn't have iPhones

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Speaker 3: at that point. They they were tracking and carving out

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Speaker 3: etching their trade and transactions, and it was basically the

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Speaker 3: idea of credit cards. And if you go to archaeologists

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Speaker 3: and anthropological experts, folks like Jonathan Mark kno Yer over

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Speaker 3: at the University of Wisconsin, Madison, you would see that

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Speaker 3: they had these kind of international agreements or inter civilization agreements,

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Speaker 3: and they had to use them out of necessity because

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Speaker 3: they were trading so much stuff. That you couldn't carry

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Speaker 3: around whatever past for money all the time, or you

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Speaker 3: couldn't you know, you couldn't strap thirty cattle on your back,

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Speaker 3: or thirty rocks or whatever.

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Speaker 4: I don't know if you guys remember this, And I

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Speaker 4: might be hallucinating this, but I'm picturing Fred Flintstone whipping

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Speaker 4: out a massive slab of granite that says credit card

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Speaker 4: and Fred Flintstone on it, and like plopping it down,

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Speaker 4: you know, on a table to purchase somethington Universe. It

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Speaker 4: might have been in one of the movies. Honestly, but

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Speaker 4: doesn't that seem like a gag they would do?

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Speaker 3: Yes, it does right for them.

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Speaker 6: They'll have a second career writing for the flint Stones.

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Speaker 3: I believe in you, dude.

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Speaker 4: I don't know if they're around anymore. I don't know

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Speaker 4: if there's a current iteration of the Flintstones, but that's

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Speaker 4: a problem.

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Speaker 5: I love that show.

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Speaker 3: I would argue we are the current iteration of the

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Speaker 3: Flintstones as.

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Speaker 6: Almost the human race.

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Speaker 2: Give us five more years, then we'll be right back

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Speaker 2: to Flintstones.

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Speaker 3: Cartoon four. World War We used to say World War

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Speaker 3: three will be fought with sticks and stones, but it's

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Speaker 3: going to be World War four, so everybody stay tuned.

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Speaker 3: We know the rules of credit are so old that

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Speaker 3: they are literally in the very first written laws of

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Speaker 3: human civilization. The Code of Hamarabi talks in depth about

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Speaker 3: a lot of legal ease, and they talk in depth

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Speaker 3: about credit and debt. But it makes sense when you

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Speaker 3: think about it, right, it's well before the age of

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Speaker 3: plastic cards and convenient smartphone apps. Nobody wanted to carry around,

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Speaker 3: to our earlier point, all those stone tablets. But we

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Speaker 3: do have to mention just to give you a taste

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Speaker 3: of how conspiratorial this gets. Fun fact, if you want

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Speaker 3: to be fun at parties, folks, The world's first known

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Speaker 3: international bank was created by Dylan drum roll if you

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Speaker 3: will perfect the Knights Templar, and.

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Speaker 6: It was called the Iron Bank.

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Speaker 4: No it is well, haven't see the new the New

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Speaker 4: Game of Throne show.

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Speaker 6: I started watching House Dragons No.

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Speaker 5: Night of the Seven Kingdoms.

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Speaker 3: Knight of the Seven Kingdoms. I know George is a

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Speaker 3: little bit irritated about this.

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Speaker 5: Was I think he likes this one.

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Speaker 4: Actually I read an interview that he was more involved

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Speaker 4: but just does not like House of Dragons.

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Speaker 3: That's one.

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Speaker 5: It seems like he likes the adaptation.

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Speaker 3: Yeah, well, Finish wins the Winter, George.

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Speaker 6: He says George, so he says he hates it anymore.

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Speaker 2: Let's just cast our minds back to the Knight's Templo

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Speaker 2: times when the Knights are rome around protecting things.

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Speaker 3: Dare we say crusading?

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Speaker 2: Yeah, crusading, maybe a bit pillaging, and yeah, all kinds

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Speaker 2: of things. But one of the things they're really good

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Speaker 2: at is protecting goods as they moved along international routes

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Speaker 2: and trade routes and things like that, and you know,

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Speaker 2: as treasure is being plundered and moved from one place

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Speaker 2: to another. These banks became pretty important because you needed

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Speaker 2: to pay for stuff at different ports, right, and depending

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Speaker 2: on where you're traveling. If you're a wealthy businessman in

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Speaker 2: the Knights templar day, sure, I wealthy merchant.

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Speaker 3: I'm the Pope at the pope point where I still

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Speaker 3: get along with the Knights templar, Yeah, I still trust

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Speaker 3: these guys to run the money because they don't run

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Speaker 3: all of it.

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Speaker 5: Yet.

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Speaker 6: Wasn't there a bit of a schism though at some

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Speaker 6: point or they started beefing.

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Speaker 5: I think that's right.

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Speaker 3: Everybody check out our earlier episodes on The Knights Templar.

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Speaker 4: And also check out Ridiculous History for some more info

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Speaker 4: on credit cards and credit scores if you wish.

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Speaker 2: But I guess the big deal here is this is

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Speaker 2: still around the time when banks are seen as a

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Speaker 2: form of security, a physical place where you take your money.

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Speaker 2: And now it is safe because they've got a big

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Speaker 2: old box that they created with iron stuff. And then

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Speaker 2: there's dudes with swords standing outside going don't take this.

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Speaker 3: And they have uniforms, which is super duper fancy for

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Speaker 3: the time, right, Like who can afford to be an

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Speaker 3: early adopter of laundry? Right, these guys?

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Speaker 2: This is still the time when there are a ton

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Speaker 2: of human beings that end up having pretty good ideas,

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Speaker 2: but they have no means of carrying those ideas out right,

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Speaker 2: No money, no influence, no whatever. It is the thing

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Speaker 2: that you need to make a big thing happen.

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Speaker 3: Right, But yeah, this one I remember these days. Yeah, yeah, I'm.

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Speaker 2: Sure you do, man, But there's so many There were

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Speaker 2: there were enough people out there with enough wealth, enough

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Speaker 2: accumulated treasure and stuff that they were like, hey, maybe

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Speaker 2: we could offer a little bit out and let you

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Speaker 2: do your thing as long as you give us a

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Speaker 2: little piece, a little taste on the other.

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Speaker 3: End, Yeah, a little vigorous right tail. As old as

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Speaker 3: time or as old as human time. Fast forward, okay,

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Speaker 3: twenty twenty six. Hopefully this age as well, your credit

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Speaker 3: card activity is going to have an impact. And this

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Speaker 3: again it goes back to haba Robbie. Through the templars,

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Speaker 3: et cetera, your idea of trustworthiness or others perceived idea

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Speaker 3: of your trustworthiness, you're a way to predict your financial

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Speaker 3: behavior is going to have an impact on what is

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Speaker 3: called your credit score, which, if we're honest, is another

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Speaker 3: ancient and ridiculous concept similar to the necktie.

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Speaker 4: Well and not entirely dissimilar from insurance and the way

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Speaker 4: people are judged to be insurable or if your driving record,

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Speaker 4: for example, which you are assigned to score too, is low,

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Speaker 4: you will pay much higher premium on your car insurance.

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Speaker 4: So it's also similar to predictive model.

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Speaker 3: Not all that stuff, all that stuff that your second

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Speaker 3: grade or third grade elementary school teacher told you about

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Speaker 3: your permanent record turns out to be true, but not

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Speaker 3: in the way they meant it. When they caught you,

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Speaker 3: you know, stealing crayons, or picking your nose or eating

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Speaker 3: For all the Marines in the crowd.

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Speaker 2: It was the Wolford family in Atlanta. They were the

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Speaker 2: ones who knew.

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Speaker 3: So you could argue a credit score is ridiculous, sure, objectively,

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Speaker 3: but maybe a necessary evil in these our modern evenings.

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Speaker 3: But how would we describe a credit score knowing that

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Speaker 3: capitalism is basically a religion. A credit score attempts to

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Speaker 3: quantify things that were always considered qualitative or moral about

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Speaker 3: human beings beforehand. It's a numerical representation of how much

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Speaker 3: trust you have earned.

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Speaker 4: I think it goes up debt. What is it eight hundred?

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Speaker 4: Is it nine hundred? And then eight fifty? Okay, that's

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Speaker 4: perfect according to.

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Speaker 3: Eight fifty for most people. Once you get to a

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Speaker 3: certain echelon of socioeconomics, you're beyond credit scores.

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Speaker 4: Well sure, and you know it's interesting too, how much

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Speaker 4: of it is counterintuitive because you know, you would think

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Speaker 4: that you could opt out and by having zero debt

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Speaker 4: or having zero credit card, you know payments, that you

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Speaker 4: would have a perfect credit score.

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Speaker 6: Not true.

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Speaker 4: It's something you have to build, which is why your

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Speaker 4: parents might have told you, like, get one credit card

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Speaker 4: when you're a teenager, they get it for you and

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Speaker 4: use it only to pay for gas and then pay

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Speaker 4: it off every month, which is a smart thing to

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Speaker 4: do because you are then establishing credit age, which is

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Speaker 4: one of the factors that go into that number.

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Speaker 3: Yeah, one of the five oh, one of the big

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Speaker 3: five to eight, because it gets not all sources agree.

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Speaker 3: A credit score, simply put, includes things like your payment history,

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Speaker 3: which goes to age of your appearance in the system,

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Speaker 3: and then your debt to your debt ratio. How much

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Speaker 3: money do you currently owe compared to the amount of

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Speaker 3: money that you could owe in the future, which is

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Speaker 3: not the same amount or not the same ratio as

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Speaker 3: the money you owe versus the money you could make

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Speaker 3: or create or acquire in the future. It's how much

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Speaker 3: how deep in the hole can this individual go and

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Speaker 3: still give us our vigorous I'm saying that because we're

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Speaker 3: all fans of the Sopranos for sure.

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Speaker 5: Yeah, be watching that lately.

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Speaker 2: Actually, Well, in there, you've also got length of credit history,

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Speaker 2: amount of new credit, and your credit mix.

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Speaker 3: Oh what's that?

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Speaker 6: What's your credit mix?

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Speaker 3: You gotta diversify your portfolio, you know what I mean?

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Speaker 3: Like it it's such a imagine you are a bank

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Speaker 3: and think of banking as someone who think of it

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Speaker 3: as a bank, as a person who is evaluating folks

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Speaker 3: they will date in an open relationship. You don't want.

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Speaker 3: It's such a turnoff, you know, when you see someone

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Speaker 3: who only has a mortgage, right, they only have one

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Speaker 3: kind of debt, they only have a car note where

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Speaker 3: their credit cards, you know what I mean, be interesting

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Speaker 3: on your profile otherwise wipe left for sure.

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Speaker 4: But also like you don't want to see someone that

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Speaker 4: has too many credit cards, even if they're paying correctly

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Speaker 4: and they don't you know, oh too much or they're

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Speaker 4: not underwater, because I think credit cards are referred to

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Speaker 4: as revolving accounts, which is one particular type of accounts,

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Speaker 4: whereas a mortgage is its own thing, car loans its

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Speaker 4: own thing. So you kind of want to have a

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Speaker 4: mix of the different types of loans and then not

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Speaker 4: too many revolving accounts, right again, diversified.

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Speaker 2: At this moment, we should all look at our credit scores,

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Speaker 2: not say them out loud, but just take note of

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Speaker 2: exactly what they are and then keep track after this

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Speaker 2: episode goes live and see if they get affected.

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Speaker 4: And because it folks with your age, it fiks with

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Speaker 4: your credit age.

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Speaker 6: Never close your oldest one.

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Speaker 5: Ever.

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Speaker 2: We are going to get into the creepiest thing, though,

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Speaker 2: is that they know personal stuff about you.

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Speaker 3: What we're saying here, folks, is this idea of a

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Speaker 3: credit score includes the number of active accounts you possess,

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Speaker 3: which sorts of accounts those are, how old they are,

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Speaker 3: how young they are. It's best to think of it

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Speaker 3: as somewhere between a financial version of a report card

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Speaker 3: or a fingerprint, and it, just like your fingerprint, it

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Speaker 3: contains a lot more detail about you than you might

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Speaker 3: cursorily imagine as real world impact. You get a credit

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Speaker 3: report based on your credit score, or you can if

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00:23:02,400 --> 00:23:06,159
Speaker 3: you manually request it. And this is a three digit

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Speaker 3: number that has a huge impact in how much interest

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Speaker 3: you'll have to pay for a given loan of any sort,

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00:23:14,640 --> 00:23:18,080
Speaker 3: as well as the types of loans or credit cards

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Speaker 3: you can get. In the first place, it quantifies again,

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00:23:22,440 --> 00:23:26,919
Speaker 3: it quantifies what was once seen as moral character, the

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Speaker 3: trust in you from institutions future actions that you may

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Speaker 3: or may not take, and that level of trust is

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00:23:35,160 --> 00:23:39,200
Speaker 3: expressed in stuff like the interest rate on a given loan,

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Speaker 3: or whether or not you can get a loan for.

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Speaker 4: Sure, and it's just you know, not particularly transparent, which

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Speaker 4: we are going to get into. And if anyone out

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Speaker 4: there is interested in having a quick look see at

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00:23:51,560 --> 00:23:54,520
Speaker 4: your credit score, you can get a credit Karma account

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00:23:54,680 --> 00:23:58,480
Speaker 4: which does not do what's called a hard inquiry for

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Speaker 4: showing you that credit. Every time you run a credit

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00:24:01,040 --> 00:24:04,040
Speaker 4: check with a getting a loan or a car loan

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Speaker 4: or whatever, you get a hard inquiry on your account,

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Speaker 4: and those hard inquiries credit to take a ding. There

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Speaker 4: isn't a neat little rule where if you're shopping for credit,

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Speaker 4: you're shopping for a loan, they only count at once.

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Speaker 4: If it's the same type of loan and the same

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Speaker 4: type of companies for the same product. That's dinging your

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Speaker 4: credit report. So don't worry too much if you're looking

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Speaker 4: for a car that each one of those credit.

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Speaker 5: Checks is going to ding your account.

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Speaker 4: But if you want to take a look, go and

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Speaker 4: get a free Credit Karma account. It's actually a pretty

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00:24:35,440 --> 00:24:36,160
Speaker 4: cool service.

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Speaker 3: We are not sponsored by Credit Karma currently, but that

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Speaker 3: is a great point technology both aids and in perils

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Speaker 3: human civilization, especially with broken systems like this. Look, we

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Speaker 3: don't have to be financial wizards or sorcerers to immediately

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Speaker 3: clock how profoundly this can impact or alter a person's life.

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Speaker 3: And given the level of importance here, it is understandable

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Speaker 3: to assume that surely someone is at the wheel. Surely

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00:25:08,600 --> 00:25:13,440
Speaker 3: there's something like a single big government agency to keep

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00:25:13,560 --> 00:25:16,560
Speaker 3: track of all of this, like the EPA or the

437
00:25:16,640 --> 00:25:20,560
Speaker 3: FDA used to keep track of environmental problems, or you

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Speaker 3: know the food that you eat shout out to the jungle,

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Speaker 3: or at.

440
00:25:24,960 --> 00:25:28,399
Speaker 2: Least maybe there's a giant a giant room somewhere filled

441
00:25:28,440 --> 00:25:30,920
Speaker 2: with files in the Pentagon where the smoking man can

442
00:25:30,960 --> 00:25:32,880
Speaker 2: go put these important docs in. No.

443
00:25:33,040 --> 00:25:37,920
Speaker 3: Yeah, something like an authority figure that agency. We prefer

444
00:25:37,960 --> 00:25:41,200
Speaker 3: an agency to an individual right would be like any

445
00:25:41,200 --> 00:25:46,520
Speaker 3: other federal agency up until recent years, subject to all

446
00:25:46,560 --> 00:25:51,760
Speaker 3: sorts of anti corruption measures, regular reviews, and so on

447
00:25:51,840 --> 00:25:54,640
Speaker 3: and so on and so forth to keep the average

448
00:25:54,680 --> 00:26:00,640
Speaker 3: American from getting unfair treatment. We are kidding. You love

449
00:26:00,720 --> 00:26:05,080
Speaker 3: that assumption, and it is the right assumption. But unfortunately

450
00:26:05,200 --> 00:26:09,960
Speaker 3: that assumption is incorrect. The current US credit system, which

451
00:26:10,000 --> 00:26:13,160
Speaker 3: impacts everybody, no matter who you vote for, no matter

452
00:26:13,200 --> 00:26:15,640
Speaker 3: what kind of music you listen to, no matter where

453
00:26:15,640 --> 00:26:21,200
Speaker 3: you live. This system depends upon not one, but multiple

454
00:26:21,800 --> 00:26:26,479
Speaker 3: private companies, and they hoover up as much of your data,

455
00:26:26,920 --> 00:26:31,639
Speaker 3: financial data and more as possible, they compile it, and

456
00:26:31,680 --> 00:26:35,479
Speaker 3: then they sell it at a profit to any lender

457
00:26:35,600 --> 00:26:39,359
Speaker 3: that is gained to buy that information. As time goes on,

458
00:26:39,840 --> 00:26:48,159
Speaker 3: this arrangement leads to serious problems. Dare we say conspiracy.

459
00:26:50,800 --> 00:26:56,400
Speaker 3: Here's where it gets crazy. That's right, folks, friends and neighbors.

460
00:26:57,320 --> 00:27:01,320
Speaker 3: If you live in the United States, our financial future

461
00:27:02,000 --> 00:27:07,320
Speaker 3: is literally determined by private entities. You do not get

462
00:27:07,359 --> 00:27:09,879
Speaker 3: to vote for their leadership. You do not get to

463
00:27:09,960 --> 00:27:14,400
Speaker 3: vote against their leadership. And to our earlier point, you

464
00:27:14,480 --> 00:27:16,600
Speaker 3: cannot not play the.

465
00:27:16,600 --> 00:27:18,080
Speaker 5: Game for sure.

466
00:27:18,119 --> 00:27:20,840
Speaker 4: And like you know, one of these types of data

467
00:27:20,880 --> 00:27:23,679
Speaker 4: analytic companies that's been around for a long time is

468
00:27:24,520 --> 00:27:28,200
Speaker 4: FCO or the fair Isaac and Company, which you can

469
00:27:28,240 --> 00:27:31,120
Speaker 4: buy publicly on the stock market. They are very much

470
00:27:31,160 --> 00:27:32,440
Speaker 4: a for profit institution.

471
00:27:33,680 --> 00:27:36,400
Speaker 2: Yeah, and if you have certain credit cards on that

472
00:27:36,520 --> 00:27:39,720
Speaker 2: credit card app, you can have direct access to FICO

473
00:27:40,160 --> 00:27:45,959
Speaker 2: and all of their fun, exciting sounding, innocuous sounding services

474
00:27:46,000 --> 00:27:47,960
Speaker 2: that check up on your credit all the time, not

475
00:27:48,080 --> 00:27:51,000
Speaker 2: just once a year. You know, the way that legislation

476
00:27:51,040 --> 00:27:52,600
Speaker 2: went through two thousand and three to let us all

477
00:27:52,640 --> 00:27:55,120
Speaker 2: have one credit report every year from all the three

478
00:27:55,160 --> 00:27:55,920
Speaker 2: big agencies.

479
00:27:55,960 --> 00:27:56,760
Speaker 3: Oh nights.

480
00:27:57,680 --> 00:28:00,280
Speaker 2: Now you can just have a constant credit report and

481
00:28:00,359 --> 00:28:04,480
Speaker 2: be like, ooh, this happened. It went down five points.

482
00:28:04,680 --> 00:28:07,440
Speaker 6: Oh, yes, for sure.

483
00:28:07,440 --> 00:28:09,800
Speaker 4: And I kind of got off the point when I

484
00:28:09,800 --> 00:28:13,520
Speaker 4: brought up credit karma. I was mentioning transparency. And one

485
00:28:13,520 --> 00:28:15,679
Speaker 4: thing that I have found out in mentioning, you know,

486
00:28:15,720 --> 00:28:18,879
Speaker 4: in various lending situations, what my credit karma score was.

487
00:28:19,160 --> 00:28:21,280
Speaker 4: Often that's given a little bit of an eye roll,

488
00:28:21,480 --> 00:28:23,720
Speaker 4: and the you know folks that are doing the lending

489
00:28:23,800 --> 00:28:26,040
Speaker 4: tend to say that it's not entirely accurate, but it's

490
00:28:26,080 --> 00:28:30,040
Speaker 4: a relatively decent gauge. So there isn't like this full

491
00:28:30,080 --> 00:28:31,240
Speaker 4: transparency with that.

492
00:28:31,280 --> 00:28:36,639
Speaker 3: Numb And here's our big reveal. The big badgers in

493
00:28:36,720 --> 00:28:43,080
Speaker 3: this bag of conspiracy are three private companies. There are others.

494
00:28:43,520 --> 00:28:47,240
Speaker 3: The ones that are impacting, as you said, Noel Fiico,

495
00:28:47,720 --> 00:28:52,080
Speaker 3: the ones that are showing up on your countless apps.

496
00:28:52,120 --> 00:28:56,080
Speaker 3: They're going to be Equifax, Experience in TransUnion, each of

497
00:28:56,120 --> 00:28:59,920
Speaker 3: which have their own fascinating histories. We're only listing the

498
00:29:00,160 --> 00:29:03,760
Speaker 3: in alphabetical order here. In twenty twenty six you will

499
00:29:03,840 --> 00:29:10,040
Speaker 3: find countless again, countless innumerable apps and services that show

500
00:29:10,120 --> 00:29:14,000
Speaker 3: you what these badgers think of you financially on a

501
00:29:14,120 --> 00:29:18,480
Speaker 3: range of three hundred to about eight point fifty, again

502
00:29:18,600 --> 00:29:22,720
Speaker 3: a three digit number from poor to excellent. And I

503
00:29:22,880 --> 00:29:27,320
Speaker 3: fantasize sometimes guys about the people who are beyond the

504
00:29:27,400 --> 00:29:31,640
Speaker 3: religion of money, right, the billionaire the real ones that

505
00:29:31,680 --> 00:29:36,280
Speaker 3: we don't hear about, Like what's the real financial worth

506
00:29:36,360 --> 00:29:41,520
Speaker 3: quote unquote of someone like Royalty or Vladimir Putin. I'm

507
00:29:41,560 --> 00:29:44,840
Speaker 3: picturing this is very anime, right, but I'm picturing someone

508
00:29:45,080 --> 00:29:48,480
Speaker 3: who has a credit score of one, and they could

509
00:29:48,520 --> 00:29:50,640
Speaker 3: do whatever they want, you know what I mean?

510
00:29:51,080 --> 00:29:54,280
Speaker 5: Interesting the grid to disconnect from society.

511
00:29:54,440 --> 00:29:56,440
Speaker 4: I didn't realize this, but I was doing a little

512
00:29:56,520 --> 00:30:00,800
Speaker 4: cursory Google, and you know businesses and you know corporations,

513
00:30:01,000 --> 00:30:03,720
Speaker 4: they do also have credit scores. There are business credit

514
00:30:03,760 --> 00:30:05,560
Speaker 4: scores and individual credit scores.

515
00:30:05,640 --> 00:30:06,560
Speaker 5: But as we'll get.

516
00:30:06,480 --> 00:30:09,800
Speaker 4: Into, not quite sure that those numbers are treated the

517
00:30:09,840 --> 00:30:12,200
Speaker 4: same or have the same level of impact, whether your

518
00:30:12,280 --> 00:30:13,520
Speaker 4: corporation or an individual.

519
00:30:14,160 --> 00:30:15,640
Speaker 2: I know we're not going to go fully into the

520
00:30:15,640 --> 00:30:18,800
Speaker 2: background of these companies. We can't. Each one would be

521
00:30:18,960 --> 00:30:22,200
Speaker 2: a full episode on their own because the storied histories

522
00:30:22,680 --> 00:30:24,680
Speaker 2: of these companies go back really far. A lot of

523
00:30:24,720 --> 00:30:28,040
Speaker 2: them go back to, let's say, the oil industry. If

524
00:30:28,040 --> 00:30:32,280
Speaker 2: we look at TransUnion just quickly, if you go back

525
00:30:32,280 --> 00:30:35,880
Speaker 2: to the roots of Standard Oil. Right, well, when oil

526
00:30:36,040 --> 00:30:39,000
Speaker 2: was going gangbusters in the United States and that Texas

527
00:30:39,000 --> 00:30:41,120
Speaker 2: Tea was doing its thing, you had to find a

528
00:30:41,160 --> 00:30:44,480
Speaker 2: way to move that oil to other places. We've talked

529
00:30:44,480 --> 00:30:48,240
Speaker 2: in the past on the show about the rail in

530
00:30:48,280 --> 00:30:51,080
Speaker 2: the United States and how important that was. Right, well,

531
00:30:51,080 --> 00:30:53,520
Speaker 2: there's this thing called the Union Tank Car Company. What

532
00:30:53,600 --> 00:30:55,520
Speaker 2: they did is they figured out how to make the

533
00:30:55,600 --> 00:31:00,600
Speaker 2: first wooden rail cars to send oil across the country.

534
00:31:01,000 --> 00:31:03,440
Speaker 2: Eventually they figured out that metal was a better idea

535
00:31:03,560 --> 00:31:06,360
Speaker 2: for you know, transferring the oil. But they started out right,

536
00:31:06,680 --> 00:31:09,840
Speaker 2: they're starting from the bottom, and just imagine the amount

537
00:31:09,880 --> 00:31:12,760
Speaker 2: of wealth and money that is accruing through that oil

538
00:31:12,840 --> 00:31:16,440
Speaker 2: industry through people like John D. Rockefeller who controlled Standard Oil,

539
00:31:16,920 --> 00:31:21,000
Speaker 2: and then all of that money that's being generated that

540
00:31:21,200 --> 00:31:23,080
Speaker 2: is now able to or at least people at the

541
00:31:23,080 --> 00:31:25,360
Speaker 2: top are thinking, how do we make more money? Is

542
00:31:25,400 --> 00:31:27,960
Speaker 2: are there other avenues to make money? And one of

543
00:31:28,000 --> 00:31:33,440
Speaker 2: those things is figuring out who is worthy of getting

544
00:31:33,560 --> 00:31:37,480
Speaker 2: the money. How did it go from oil companies like that,

545
00:31:37,760 --> 00:31:40,280
Speaker 2: or let's say, if you look at another one, Equifax

546
00:31:40,440 --> 00:31:44,080
Speaker 2: and the Wolford family in Atlanta back in eighteen ninety nine,

547
00:31:44,760 --> 00:31:49,040
Speaker 2: same type of thing, figuring out how do we make

548
00:31:49,080 --> 00:31:52,479
Speaker 2: a company out of figuring out who is worthy of

549
00:31:52,520 --> 00:31:56,320
Speaker 2: getting all of this money we've accrued? Right, right, And

550
00:31:56,520 --> 00:31:58,960
Speaker 2: so to me, the big conspiracy is that it's a

551
00:31:59,000 --> 00:32:01,720
Speaker 2: method of control, right by these people who are already

552
00:32:01,720 --> 00:32:05,240
Speaker 2: in power, who were obscenely wealthy back in the eighteen

553
00:32:05,320 --> 00:32:10,120
Speaker 2: hundred early nineteen hundreds, figuring out how do they decide

554
00:32:10,160 --> 00:32:11,480
Speaker 2: and control who gets the money.

555
00:32:11,880 --> 00:32:14,560
Speaker 4: And that's kind of happening in tandem with more elaborate

556
00:32:14,680 --> 00:32:18,800
Speaker 4: versions of lending and extending credit and financial products, and

557
00:32:18,800 --> 00:32:21,000
Speaker 4: all of a sudden, now you've got this industry that

558
00:32:21,080 --> 00:32:24,640
Speaker 4: needs some metric on how we divvy those pieces of

559
00:32:24,640 --> 00:32:26,600
Speaker 4: the pie up, you know, to the people that are

560
00:32:26,640 --> 00:32:29,680
Speaker 4: like below the oil magnates, how do we include them

561
00:32:29,720 --> 00:32:31,719
Speaker 4: but also make as much money off of them as

562
00:32:31,800 --> 00:32:32,560
Speaker 4: humanly possible?

563
00:32:32,920 --> 00:32:37,640
Speaker 3: Yeah, again, the templars right established pattern. Find that pattern.

564
00:32:37,720 --> 00:32:41,800
Speaker 3: It also reminds me. I don't want to call you

565
00:32:41,840 --> 00:32:44,120
Speaker 3: out here, NOL, but I think you'll appreciate this one.

566
00:32:44,600 --> 00:32:47,760
Speaker 3: I think you'll appreciate too, Matt and Dylan. I was

567
00:32:47,800 --> 00:32:51,760
Speaker 3: recently thinking about gift cards, something as innocuous as a

568
00:32:51,800 --> 00:32:57,000
Speaker 3: gift card, like a Starbucks card, a Target card. Not

569
00:32:57,160 --> 00:33:00,480
Speaker 3: just dunking on those. But insert dunkin Donuts while we're

570
00:33:00,480 --> 00:33:03,440
Speaker 3: at it. Insert any company with a gift card.

571
00:33:03,560 --> 00:33:06,520
Speaker 4: But are you referring to me my historical perspective that

572
00:33:07,080 --> 00:33:09,000
Speaker 4: gift cards are a terrible, thoughtless gift.

573
00:33:09,200 --> 00:33:14,120
Speaker 2: Oh you guys, remember we used to get iTunes like

574
00:33:14,200 --> 00:33:17,080
Speaker 2: twenty dollars iTunes gift cards from the company way back

575
00:33:17,120 --> 00:33:17,600
Speaker 2: in the day.

576
00:33:18,360 --> 00:33:20,200
Speaker 6: Yeah, it's the kind of gift the company would give.

577
00:33:20,240 --> 00:33:23,840
Speaker 3: It's the gift. Yeah, for sure, it's the gift of

578
00:33:24,080 --> 00:33:28,200
Speaker 3: distant aunts would give, which is description.

579
00:33:28,680 --> 00:33:30,240
Speaker 4: I don't want to be limited to where I can

580
00:33:30,400 --> 00:33:33,600
Speaker 4: use my card. Just give me cash in an envelope.

581
00:33:33,680 --> 00:33:37,320
Speaker 3: Here's the grift. And agreed, right, I don't care about

582
00:33:37,320 --> 00:33:40,120
Speaker 3: which currency it is from the world, you know, give me,

583
00:33:40,240 --> 00:33:43,760
Speaker 3: give me the main coupons. Here's why gift cards are

584
00:33:43,800 --> 00:33:47,120
Speaker 3: a grift. This is a conspiracy that's dive in an episode.

585
00:33:47,400 --> 00:33:49,239
Speaker 3: We just live in this world and we need you

586
00:33:49,320 --> 00:33:50,120
Speaker 3: to know about.

587
00:33:49,920 --> 00:33:50,400
Speaker 5: It, folks.

588
00:33:51,160 --> 00:33:55,280
Speaker 3: Some of these big companies love giving out gift cards, right.

589
00:33:55,320 --> 00:34:00,000
Speaker 3: They're around at the impulse bisection of every grocery store.

590
00:34:00,080 --> 00:34:00,400
Speaker 6: Pause.

591
00:34:00,880 --> 00:34:05,040
Speaker 3: The company takes that expense and invest it and makes

592
00:34:05,200 --> 00:34:10,160
Speaker 3: money off the interest and knows, gosh darn well that

593
00:34:10,360 --> 00:34:13,879
Speaker 3: a certain percentage of people will never use the gift card.

594
00:34:14,520 --> 00:34:18,160
Speaker 3: It's a brilliant way of making secondary revenue, just like

595
00:34:18,280 --> 00:34:22,879
Speaker 3: insurance companies, just like a lot of the data gatherers.

596
00:34:22,960 --> 00:34:26,600
Speaker 3: Check out our earlier episode about how much your data

597
00:34:26,680 --> 00:34:28,200
Speaker 3: is worth individually.

598
00:34:28,520 --> 00:34:31,080
Speaker 4: I'd love to see the ledgers that a big corporation

599
00:34:31,239 --> 00:34:34,080
Speaker 4: has of how much money is floating out there in

600
00:34:34,160 --> 00:34:37,399
Speaker 4: gift card form, and then how they reconcile with that

601
00:34:37,560 --> 00:34:40,560
Speaker 4: with the potential of people not ever using them or

602
00:34:40,640 --> 00:34:43,440
Speaker 4: losing What that calculation is. It's got to be some

603
00:34:43,600 --> 00:34:45,920
Speaker 4: serious sithematical jiu jitsu.

604
00:34:46,400 --> 00:34:49,479
Speaker 3: It's mentat stuff. Shout out to Doune dude.

605
00:34:49,560 --> 00:34:56,600
Speaker 4: Yes, mentats are the They're the mentats, the freshman.

606
00:34:58,000 --> 00:35:02,600
Speaker 3: Post butlerry and jihad mentaiti case, the thinking machines. I

607
00:35:02,640 --> 00:35:04,760
Speaker 3: am fun at parties. Boom.

608
00:35:04,880 --> 00:35:07,520
Speaker 2: That's what it's up your dune reference for the day,

609
00:35:07,600 --> 00:35:13,880
Speaker 2: and the episode brought to you by Equifax. So guys,

610
00:35:14,000 --> 00:35:18,320
Speaker 2: there's this whole thing back in the day. These companies,

611
00:35:18,400 --> 00:35:22,880
Speaker 2: because we're talking about some of them got started in

612
00:35:23,120 --> 00:35:25,279
Speaker 2: you know, the early nineteen hundred. Some of them switch away.

613
00:35:25,360 --> 00:35:28,840
Speaker 2: Equifax changed to Equifax in nineteen seventy five, but before

614
00:35:28,880 --> 00:35:34,000
Speaker 2: it was the retail credit company. And these companies used

615
00:35:34,400 --> 00:35:37,120
Speaker 2: physical means. They use the technology of the day in

616
00:35:37,120 --> 00:35:40,360
Speaker 2: physical means to gather information on people, right.

617
00:35:40,560 --> 00:35:43,279
Speaker 3: Yeah, a, which is the original sin of the problem.

618
00:35:44,000 --> 00:35:46,240
Speaker 3: What are the problems we'll get to for sure.

619
00:35:46,920 --> 00:35:49,840
Speaker 2: Oh yeah. But as we move into the digital age,

620
00:35:50,160 --> 00:35:53,959
Speaker 2: every single one, well especially these three big companies begin

621
00:35:54,160 --> 00:35:58,839
Speaker 2: acquiring tech companies, specifically tech companies that are really good

622
00:35:58,920 --> 00:36:03,120
Speaker 2: at tracking data and usage of things and how you

623
00:36:03,400 --> 00:36:06,360
Speaker 2: live your life, all the little things you do to

624
00:36:06,440 --> 00:36:09,120
Speaker 2: make up that score. So as we continue down this

625
00:36:09,239 --> 00:36:13,799
Speaker 2: list of the craziness, just remember that this has evolved

626
00:36:14,040 --> 00:36:20,520
Speaker 2: quickly since around two thousand and three, actually hit the jaker,

627
00:36:21,040 --> 00:36:25,360
Speaker 2: Yeah exactly. That's when these companies become the all knowing,

628
00:36:25,400 --> 00:36:28,920
Speaker 2: all seeing data collection vacuums that they are.

629
00:36:28,960 --> 00:36:34,200
Speaker 3: Now I've sour yes, we know. We've got to step

630
00:36:34,200 --> 00:36:37,440
Speaker 3: back for a second here and know, aside from what

631
00:36:37,480 --> 00:36:41,360
Speaker 3: we're saying. I agree these each of these big badgers

632
00:36:42,000 --> 00:36:46,200
Speaker 3: can be their own episode either on stuff they don't

633
00:36:46,239 --> 00:36:49,440
Speaker 3: want you to know or sister show ridiculous History. Also

634
00:36:49,520 --> 00:36:52,279
Speaker 3: the gift card thing, that's a gift card from us

635
00:36:52,320 --> 00:36:55,719
Speaker 3: to you. Be very careful about that around the holidays.

636
00:36:56,239 --> 00:36:59,400
Speaker 3: We do have to know in the interest of objectivity.

637
00:37:00,080 --> 00:37:04,560
Speaker 3: To the earlier point, credit reporting bureaus in the United States,

638
00:37:04,560 --> 00:37:07,680
Speaker 3: they are not just three. I'm doing the I watched

639
00:37:07,719 --> 00:37:10,799
Speaker 3: in Glorious Bastard, So I'm very careful with my threes now.

640
00:37:11,440 --> 00:37:17,040
Speaker 3: But there is another genre of a very similar thing

641
00:37:17,120 --> 00:37:20,680
Speaker 3: to that earlier point called a credit rating agency or

642
00:37:20,719 --> 00:37:25,160
Speaker 3: credit rating agencies, which do essentially the same thing, but

643
00:37:25,320 --> 00:37:29,880
Speaker 3: with the finances of companies and nation states. So again,

644
00:37:30,040 --> 00:37:32,760
Speaker 3: even if you're a country, it's difficult to opt out.

645
00:37:32,640 --> 00:37:33,120
Speaker 5: Of the game.

646
00:37:34,239 --> 00:37:37,439
Speaker 2: Yeah, And that's like Standard and Poors And I don't

647
00:37:37,480 --> 00:37:39,359
Speaker 2: know what are there any others we can list right

648
00:37:39,360 --> 00:37:41,480
Speaker 2: here of that type you're talking about, because they are

649
00:37:41,880 --> 00:37:45,239
Speaker 2: similar and some of them do very like the same things,

650
00:37:45,280 --> 00:37:48,799
Speaker 2: like both private and corporate. And it gets really, it

651
00:37:48,800 --> 00:37:51,279
Speaker 2: gets hairy when you try and dig down into it.

652
00:37:51,520 --> 00:37:53,520
Speaker 4: Yeah, and it just gets so confusing too, because you know,

653
00:37:53,719 --> 00:37:57,640
Speaker 4: we hear about people having their companies being downgraded or

654
00:37:58,120 --> 00:38:01,640
Speaker 4: country is even being downgraded. And you know, we talked

655
00:38:01,680 --> 00:38:05,120
Speaker 4: a little bit about how the credit bureaus look at

656
00:38:05,160 --> 00:38:08,759
Speaker 4: scores of individual humans. But then of course corporations do

657
00:38:08,920 --> 00:38:11,560
Speaker 4: have a form of a credit score. But what about

658
00:38:11,600 --> 00:38:15,200
Speaker 4: countries that have a massive deficit and that's sort of

659
00:38:15,200 --> 00:38:18,680
Speaker 4: by design owing other countries' money. And it's all this

660
00:38:18,840 --> 00:38:22,160
Speaker 4: imaginary kind of web of like you know, robbing Peter

661
00:38:22,280 --> 00:38:24,879
Speaker 4: to pay Paul, and it just gets it, really does,

662
00:38:25,040 --> 00:38:27,359
Speaker 4: matt to your point kind of make your head spin.

663
00:38:28,440 --> 00:38:32,200
Speaker 3: And I do have to recuse myself in the earlier

664
00:38:32,280 --> 00:38:37,479
Speaker 3: question as no spoilers currently in a country where I'm

665
00:38:37,600 --> 00:38:39,839
Speaker 3: not going to talk about that stuff until I get

666
00:38:39,840 --> 00:38:44,640
Speaker 3: back to the States. Moving on, you know, granted, if

667
00:38:44,719 --> 00:38:50,240
Speaker 3: everything what do what what do you mean? What? Moving on? Matthew? No,

668
00:38:50,400 --> 00:38:54,960
Speaker 3: what do you mean? Everything is above board? We make

669
00:38:55,000 --> 00:38:59,880
Speaker 3: it back? If the credit bureau stuff works, then are

670
00:39:00,480 --> 00:39:04,000
Speaker 3: it's a win win right for institutions and individuals alike.

671
00:39:04,320 --> 00:39:07,640
Speaker 3: You know, if you're a lender, you can avoid someone

672
00:39:07,719 --> 00:39:11,200
Speaker 3: defaulting on a loan, and if you're an individual, right

673
00:39:11,560 --> 00:39:15,640
Speaker 3: you will have a guardrail that stops you from falling

674
00:39:15,800 --> 00:39:19,280
Speaker 3: deeper and deeper into a hole into some feedback loop

675
00:39:19,640 --> 00:39:24,560
Speaker 3: of unpayable interest right and or buros of debt. The

676
00:39:24,600 --> 00:39:28,319
Speaker 3: issue is, as we've been establishing here, everything is not

677
00:39:28,640 --> 00:39:33,879
Speaker 3: above board. These systems have so many cartoonishly disastrous problems.

678
00:39:34,280 --> 00:39:38,840
Speaker 3: We looked into the research, We read a lot of stuff.

679
00:39:40,120 --> 00:39:45,920
Speaker 3: Government officials, journalists, consumer advocates, tons of academics have pointed

680
00:39:45,960 --> 00:39:50,000
Speaker 3: out these problems in general and in specific, over and

681
00:39:50,200 --> 00:39:53,440
Speaker 3: over and over again. There's so many problems in fact,

682
00:39:53,440 --> 00:39:57,160
Speaker 3: that we we should explore them as separate categories all

683
00:39:57,200 --> 00:40:00,440
Speaker 3: their own. They're like spokes on an insidious way wheel

684
00:40:00,880 --> 00:40:04,680
Speaker 3: rolling over innocent people, or you know, fingers what a

685
00:40:04,840 --> 00:40:10,160
Speaker 3: shadowy hand right at your financial neck. Okay, first off, opacity,

686
00:40:10,360 --> 00:40:14,240
Speaker 3: This probably bugs us all the most. Do you guys

687
00:40:14,280 --> 00:40:19,120
Speaker 3: think the credit reports are intentionally difficult for the average

688
00:40:19,120 --> 00:40:20,799
Speaker 3: barrower to understand?

689
00:40:21,640 --> 00:40:21,920
Speaker 5: Yes?

690
00:40:22,360 --> 00:40:23,000
Speaker 3: I think so.

691
00:40:23,520 --> 00:40:28,040
Speaker 4: It's the same way that government, and you know, various

692
00:40:29,120 --> 00:40:33,600
Speaker 4: methods of influencing policy and politics are also incredibly vague

693
00:40:33,640 --> 00:40:37,080
Speaker 4: and it's very difficult to figure out, you know, where

694
00:40:37,120 --> 00:40:40,680
Speaker 4: you can actually exercise some agency and all of these things,

695
00:40:41,040 --> 00:40:43,000
Speaker 4: and the way things are hidden in bills and all

696
00:40:43,040 --> 00:40:46,399
Speaker 4: of that. Yes, it's all entirely designed to be as

697
00:40:46,560 --> 00:40:48,799
Speaker 4: non user friendly as possible, like the tax code, Like

698
00:40:48,800 --> 00:40:51,319
Speaker 4: I mean, it's all just an esoteric af.

699
00:40:51,880 --> 00:40:55,880
Speaker 2: Well, and similar to trying to think is there a

700
00:40:55,880 --> 00:40:59,880
Speaker 2: one to one anywhere in our lives? We're not allowed.

701
00:41:00,239 --> 00:41:03,200
Speaker 2: We're not. We weren't for a long time allowed to

702
00:41:03,239 --> 00:41:05,600
Speaker 2: know exactly what was in our credit report, like the

703
00:41:05,640 --> 00:41:09,399
Speaker 2: full thing, all the details, until two thousand and three

704
00:41:09,840 --> 00:41:13,839
Speaker 2: when it was kind of cemented with legislation and what

705
00:41:13,920 --> 00:41:15,920
Speaker 2: is in the name of that, the Fair Credit Reporting

706
00:41:15,960 --> 00:41:18,560
Speaker 2: Act of nineteen seventy and then in two thousand and three,

707
00:41:18,640 --> 00:41:22,040
Speaker 2: the Fair and Accurate Credit Transactions Act of two thousand

708
00:41:22,080 --> 00:41:26,120
Speaker 2: and three. So the first one gave consumers the right

709
00:41:26,480 --> 00:41:29,760
Speaker 2: to know the information stored about them in corporate data banks.

710
00:41:30,000 --> 00:41:32,240
Speaker 2: But again, how much of that information did we actually

711
00:41:32,280 --> 00:41:34,160
Speaker 2: get to learn about that's stored in there?

712
00:41:34,600 --> 00:41:38,680
Speaker 3: Yeah, and it was not proactively granted. It gave you

713
00:41:38,760 --> 00:41:41,000
Speaker 3: the right to manually request it.

714
00:41:42,120 --> 00:41:45,400
Speaker 2: Yes, yeah, exactly. It's not like it's just open information anymore.

715
00:41:45,440 --> 00:41:47,520
Speaker 2: And thank god, is not open information because you don't

716
00:41:47,560 --> 00:41:49,879
Speaker 2: want everybody to know all of that stuff. But some

717
00:41:49,960 --> 00:41:53,480
Speaker 2: corporate entity. Maybe three of them know all of that stuff.

718
00:41:53,880 --> 00:41:55,600
Speaker 2: But then the one for two thousand and three gave

719
00:41:55,640 --> 00:41:57,799
Speaker 2: us the right to get one every year from each

720
00:41:57,840 --> 00:41:59,799
Speaker 2: of the reporting agencies.

721
00:42:00,480 --> 00:42:04,400
Speaker 6: Upon request, request or something.

722
00:42:04,520 --> 00:42:08,200
Speaker 2: Right, Yeah, well, because you had I think experience. Oh god,

723
00:42:08,239 --> 00:42:09,960
Speaker 2: I can't remember. I think it was experience. They're the

724
00:42:10,000 --> 00:42:12,200
Speaker 2: ones who got their handslapped of the hardest because they

725
00:42:12,239 --> 00:42:15,640
Speaker 2: were trying to sell everybody for like seventy dollars your

726
00:42:15,640 --> 00:42:17,640
Speaker 2: credit report or something crazy.

727
00:42:18,480 --> 00:42:19,320
Speaker 6: We're the privilege.

728
00:42:19,920 --> 00:42:22,920
Speaker 3: Well, let's let's exercise empathy, folks. And this is going

729
00:42:23,000 --> 00:42:26,040
Speaker 3: to be common to a lot of us tuning in tonight.

730
00:42:26,480 --> 00:42:30,720
Speaker 3: So let's say you're doing everything right. You have played

731
00:42:30,760 --> 00:42:33,880
Speaker 3: the game right. You maybe you paid off your student

732
00:42:33,960 --> 00:42:36,680
Speaker 3: loans and your credit score dipped for a second and

733
00:42:36,719 --> 00:42:41,000
Speaker 3: you're like why, And then it repaired over time, right,

734
00:42:41,360 --> 00:42:45,839
Speaker 3: it started to grow back up into the rarefied air

735
00:42:45,880 --> 00:42:49,879
Speaker 3: of excellent credit. And you check stuff one day because

736
00:42:49,880 --> 00:42:53,239
Speaker 3: you're proactive, right, you're on your side if no one else,

737
00:42:53,600 --> 00:42:57,319
Speaker 3: and you see your score dropped suddenly. In a microcosm

738
00:42:57,360 --> 00:43:01,680
Speaker 3: for a perfect weird analogy, here, you're dating someone. Everything's

739
00:43:01,719 --> 00:43:04,520
Speaker 3: going great. You wake up one morning and they're like,

740
00:43:04,760 --> 00:43:08,640
Speaker 3: you Stink're like, oh, I took a shower yesterday, and

741
00:43:08,640 --> 00:43:11,200
Speaker 3: they said, no, you stink is a person I don't

742
00:43:11,239 --> 00:43:15,600
Speaker 3: decided personality your whole thing. Yeah. Yeah, And you're like, well,

743
00:43:16,120 --> 00:43:18,279
Speaker 3: what did I do? They say, Well, if you have

744
00:43:18,400 --> 00:43:20,720
Speaker 3: to ask, then that makes you worse.

745
00:43:21,320 --> 00:43:23,200
Speaker 4: Well, if we're, if we're, if we're taking a little

746
00:43:23,200 --> 00:43:25,840
Speaker 4: moment to talk about this scenario and actual relationships, just

747
00:43:25,840 --> 00:43:28,360
Speaker 4: don't bother worrying because you'll beat yourself up over it

748
00:43:28,400 --> 00:43:29,040
Speaker 4: and you'll never know.

749
00:43:29,360 --> 00:43:30,160
Speaker 6: You can never know.

750
00:43:30,480 --> 00:43:34,680
Speaker 4: So it's similar to you know, for analogous purple purposes,

751
00:43:34,880 --> 00:43:39,239
Speaker 4: you can also never know the true details of what

752
00:43:39,360 --> 00:43:40,680
Speaker 4: that number represents.

753
00:43:41,120 --> 00:43:45,360
Speaker 3: Right, Yeah, it can. To our point about the free

754
00:43:45,360 --> 00:43:50,520
Speaker 3: some of this legislation, it could be historically difficult, damnably

755
00:43:50,719 --> 00:43:54,759
Speaker 3: difficult to learn why the credit score changed. Right from

756
00:43:54,800 --> 00:43:58,400
Speaker 3: your perspective, you have not altered your behavior in anything

757
00:43:58,480 --> 00:44:02,480
Speaker 3: but a positive way. If you cannot figure out the

758
00:44:02,600 --> 00:44:05,120
Speaker 3: root of a problem or why an issue is occurring,

759
00:44:05,400 --> 00:44:08,160
Speaker 3: you're going to have a devil of a time fixing it. Right.

760
00:44:08,480 --> 00:44:12,200
Speaker 3: The optuse nature of these systems comes about for a

761
00:44:12,239 --> 00:44:17,080
Speaker 3: simple reason. They are each different. They're designed on the

762
00:44:17,120 --> 00:44:20,520
Speaker 3: same idea, just the same way that the metric system

763
00:44:21,040 --> 00:44:26,280
Speaker 3: and the weird system we have in the US attempts

764
00:44:26,320 --> 00:44:29,080
Speaker 3: to measure distance. Right, they call it different names, but

765
00:44:29,120 --> 00:44:31,799
Speaker 3: they're still looking at length and weight and so on.

766
00:44:32,719 --> 00:44:35,920
Speaker 3: The issue with these systems, all of them, is that

767
00:44:36,680 --> 00:44:40,680
Speaker 3: they are designed for lenders, not for you. If you're

768
00:44:40,719 --> 00:44:44,719
Speaker 3: watching this and you are human, they are not designed

769
00:44:44,719 --> 00:44:48,320
Speaker 3: for you. The credit reports are riddled with complex jargon,

770
00:44:48,719 --> 00:44:52,080
Speaker 3: weird codes that are not intuitive to the average person.

771
00:44:52,360 --> 00:44:57,000
Speaker 3: And that is because you are not their customer. You

772
00:44:57,320 --> 00:45:01,879
Speaker 3: and your data are their product. Okay, So like, if

773
00:45:01,880 --> 00:45:06,000
Speaker 3: we're making widgets, do we care what the widget thinks

774
00:45:06,560 --> 00:45:08,040
Speaker 3: of what happens at the factory?

775
00:45:09,160 --> 00:45:12,839
Speaker 2: And don't take that lightly because you can look at

776
00:45:12,880 --> 00:45:18,480
Speaker 2: something like Equifax's mosaic platform that they've got, and you

777
00:45:18,520 --> 00:45:24,200
Speaker 2: can look at politicians who look at that for election data,

778
00:45:24,640 --> 00:45:27,719
Speaker 2: at big companies who look at that same information to

779
00:45:27,760 --> 00:45:31,399
Speaker 2: see how to target people for both ads and for

780
00:45:31,520 --> 00:45:34,800
Speaker 2: where to put up next fast food joint or whatever.

781
00:45:35,200 --> 00:45:40,879
Speaker 2: And people use that kind of platform. People corporations use

782
00:45:40,920 --> 00:45:42,240
Speaker 2: that kind of platform.

783
00:45:41,800 --> 00:45:43,400
Speaker 6: To major decisions.

784
00:45:44,480 --> 00:45:48,680
Speaker 2: Yeah, yeah, people find but these corporations are using you.

785
00:45:48,800 --> 00:45:52,120
Speaker 2: Just as Ben said, as a as a tiny little

786
00:45:52,239 --> 00:45:57,080
Speaker 2: drop in their huge sea of data to sell other

787
00:45:57,120 --> 00:45:59,319
Speaker 2: people and their other products and.

788
00:45:59,440 --> 00:46:03,319
Speaker 3: Additional it's not just one big bad, It's not just

789
00:46:03,440 --> 00:46:08,279
Speaker 3: one sith Lord. We have so many private cooks in

790
00:46:08,400 --> 00:46:12,399
Speaker 3: the same profit driven kitchen. They don't get along, right,

791
00:46:13,000 --> 00:46:16,920
Speaker 3: They've got the profit motive becomes their ego, right, and

792
00:46:17,040 --> 00:46:22,120
Speaker 3: this analogy, so life altering data can be spread against

793
00:46:22,120 --> 00:46:26,879
Speaker 3: these or across these different bureaus with no consolidation. Don't

794
00:46:26,920 --> 00:46:31,799
Speaker 3: worry if it's confusing. These agencies proposed to fix the

795
00:46:31,920 --> 00:46:36,960
Speaker 3: problem they created by also pushing you to pay extra

796
00:46:37,280 --> 00:46:40,960
Speaker 3: for a subscription service or to sacrifice more of your

797
00:46:41,040 --> 00:46:44,680
Speaker 3: data or donate it, you know, like the vampires run

798
00:46:44,760 --> 00:46:47,480
Speaker 3: the blood bank. They do this instead of the free

799
00:46:47,520 --> 00:46:54,320
Speaker 3: reports the government has mandated they provide you upon request.

800
00:46:54,640 --> 00:46:58,120
Speaker 3: This leads us to hidden history. We've teased it a bit,

801
00:46:58,280 --> 00:47:01,080
Speaker 3: big criticism, and this is very important to us, and

802
00:47:01,120 --> 00:47:05,040
Speaker 3: we hope it's important to you. There is pretty stark

803
00:47:05,239 --> 00:47:13,560
Speaker 3: evidence that US credit bureaus perpetuate historical systemic discrimination and racism.

804
00:47:13,840 --> 00:47:17,160
Speaker 3: As we were discussing off air, we're talking about what

805
00:47:17,239 --> 00:47:21,120
Speaker 3: I compare it to pinkertons redlining. Oh sorry, Yeah, no, no,

806
00:47:21,280 --> 00:47:25,720
Speaker 3: perfect Pinkerton's man Redlining cove on. There's a great article,

807
00:47:25,960 --> 00:47:29,560
Speaker 3: a primer on this, you can read if you want

808
00:47:29,560 --> 00:47:32,040
Speaker 3: to quick catch up on it from The Guardian back

809
00:47:32,080 --> 00:47:35,960
Speaker 3: in twenty fifteen. And Noel, she's talking. The author of

810
00:47:35,960 --> 00:47:40,560
Speaker 3: the journalist, Sarah Ludwig, is talking about exactly what you're describing.

811
00:47:40,800 --> 00:47:46,399
Speaker 3: US banks refusing to provide loans or basic infrastructure. We're

812
00:47:46,440 --> 00:47:51,200
Speaker 3: not building our bank here in this neighborhood because of redlining, right.

813
00:47:51,239 --> 00:47:53,440
Speaker 3: Could you tell us a little more about redlining?

814
00:47:53,920 --> 00:47:56,480
Speaker 4: Yeah, I mean I couldn't get into the minutia of it,

815
00:47:56,520 --> 00:47:59,120
Speaker 4: but in general, it just has to do with considering

816
00:47:59,200 --> 00:48:05,680
Speaker 4: certain areas based on demographic information regarding race and income,

817
00:48:06,560 --> 00:48:10,120
Speaker 4: and using that to exclude individuals.

818
00:48:09,400 --> 00:48:11,759
Speaker 5: Who reside in those areas.

819
00:48:11,680 --> 00:48:14,959
Speaker 4: On the basis of their location, which is inherently tied

820
00:48:15,000 --> 00:48:20,080
Speaker 4: to exclusionary racist principles identity.

821
00:48:20,239 --> 00:48:25,359
Speaker 3: Yes, especially black and Latino neighborhoods in the US. And

822
00:48:25,400 --> 00:48:31,120
Speaker 3: then this gets okay, So first off, I'm a household

823
00:48:31,239 --> 00:48:35,400
Speaker 3: of four, right, and I'm in a neighborhood that the

824
00:48:35,440 --> 00:48:39,919
Speaker 3: banks don't like historically, so they're not going to build

825
00:48:39,920 --> 00:48:44,120
Speaker 3: a branch there even if I have done the American

826
00:48:44,239 --> 00:48:47,200
Speaker 3: dream and created a small business and would be a

827
00:48:47,320 --> 00:48:54,879
Speaker 3: very good customer. I am sol would be the appropriate abbreviation,

828
00:48:55,239 --> 00:48:57,600
Speaker 3: because we're still figuring out how much we can curse

829
00:48:57,760 --> 00:49:02,200
Speaker 3: on Netflix. This is compound by deregulation and it leads

830
00:49:02,280 --> 00:49:08,600
Speaker 3: these more predatory alternatives payday loans, the serious interest rates,

831
00:49:08,680 --> 00:49:12,279
Speaker 3: and then of course check cashing outfits. They take the

832
00:49:12,400 --> 00:49:16,000
Speaker 3: king's portion of your paycheck in a percentage for quote

833
00:49:16,040 --> 00:49:17,120
Speaker 3: unquote processing.

834
00:49:17,760 --> 00:49:21,840
Speaker 4: Even like if you have venmo or cash app for example,

835
00:49:22,120 --> 00:49:24,920
Speaker 4: if you want that money transferred to your bank account,

836
00:49:25,400 --> 00:49:27,719
Speaker 4: it costs you something to get it done day of

837
00:49:28,400 --> 00:49:30,840
Speaker 4: as opposed to waiting three or four days and it

838
00:49:30,960 --> 00:49:34,120
Speaker 4: be free. Maybe the people that make the least amount

839
00:49:34,160 --> 00:49:37,040
Speaker 4: of money need it the quickest, and they're making a

840
00:49:37,040 --> 00:49:39,279
Speaker 4: little veig off of that to get the money that

841
00:49:39,320 --> 00:49:40,319
Speaker 4: you already are due.

842
00:49:40,680 --> 00:49:41,360
Speaker 5: You know it's.

843
00:49:41,320 --> 00:49:46,600
Speaker 3: Yeah, and get this guys, Those same banks responsible for

844
00:49:46,640 --> 00:49:53,080
Speaker 3: creating this problem usually own or finance those same payday

845
00:49:53,160 --> 00:49:58,160
Speaker 3: loans and predatory check cashing outfits. Yeah.

846
00:49:58,239 --> 00:50:02,720
Speaker 2: Often the creeviiest thing for me, guys, is that these

847
00:50:02,800 --> 00:50:06,959
Speaker 2: organizations that collect all this data can empower an individual,

848
00:50:07,120 --> 00:50:10,319
Speaker 2: say someone who's in charge of a smaller bank or

849
00:50:10,320 --> 00:50:13,600
Speaker 2: an organization like a bigger bank. This data can empower

850
00:50:13,719 --> 00:50:17,760
Speaker 2: those groups to make those decisions like, oh, we don't

851
00:50:17,880 --> 00:50:21,520
Speaker 2: like you individually, person on paper here, so you cannot

852
00:50:21,600 --> 00:50:25,080
Speaker 2: move in. You cannot buy property in this county or

853
00:50:25,120 --> 00:50:27,360
Speaker 2: in this district or in this area, right.

854
00:50:27,800 --> 00:50:31,480
Speaker 3: Or even individual landlords, thou shalt not rent this apartment.

855
00:50:31,840 --> 00:50:34,640
Speaker 2: Oh yeah, absolutely, And and it goes so much deeper

856
00:50:34,719 --> 00:50:37,760
Speaker 2: even if you go to Transunion's True Audience, which takes

857
00:50:37,840 --> 00:50:39,600
Speaker 2: all of that stuff in and again figures out what

858
00:50:39,640 --> 00:50:42,040
Speaker 2: ads you should look you should be looking at, I

859
00:50:42,080 --> 00:50:45,359
Speaker 2: guess because of the previous transactions you've made on your

860
00:50:45,360 --> 00:50:48,359
Speaker 2: credit cards, because those groups can kind of see what

861
00:50:48,360 --> 00:50:51,560
Speaker 2: you're into by whatever it is that your credit card

862
00:50:51,600 --> 00:50:54,520
Speaker 2: pays for. They're like, oh, this person, we already have

863
00:50:54,520 --> 00:50:56,680
Speaker 2: all that demographic data we're talking about, but we also

864
00:50:56,760 --> 00:51:00,200
Speaker 2: know what this person likes, and now we can sell

865
00:51:00,239 --> 00:51:02,359
Speaker 2: them all that stuff. But we also don't like what

866
00:51:02,400 --> 00:51:05,360
Speaker 2: they like, so now they can't move in here. It's

867
00:51:05,440 --> 00:51:07,960
Speaker 2: all connected, and it's all possible there because of the

868
00:51:07,960 --> 00:51:09,320
Speaker 2: amount of data being collected.

869
00:51:09,920 --> 00:51:14,480
Speaker 3: Right, as Flannery O'Connor once said, all things that rise converge, right,

870
00:51:15,239 --> 00:51:19,680
Speaker 3: especially your information, folks. It's deeper than hip hop. We

871
00:51:19,800 --> 00:51:25,800
Speaker 3: know that, we know. Another example of this would be ultimate.

872
00:51:25,960 --> 00:51:29,800
Speaker 3: The ultimate example of this is what is sometimes called

873
00:51:29,880 --> 00:51:35,040
Speaker 3: a two tiered financial system. So, with these insane rates

874
00:51:35,120 --> 00:51:40,280
Speaker 3: or with this soft discrimination, which is a technologically driven

875
00:51:40,360 --> 00:51:44,960
Speaker 3: discrimination that legislation has yet to catch up with. With

876
00:51:45,080 --> 00:51:51,120
Speaker 3: these insane rates, zurious percentages, crazy interest, people in these

877
00:51:51,160 --> 00:51:55,520
Speaker 3: communities become much more likely to fall behind or to

878
00:51:55,760 --> 00:51:59,959
Speaker 3: indeed default on a loan this rexy person's credit score,

879
00:52:00,080 --> 00:52:04,680
Speaker 3: or it creates a uru burus of inescapable debt, and thus,

880
00:52:05,120 --> 00:52:08,480
Speaker 3: through no faults of their own, through no flaw in

881
00:52:08,640 --> 00:52:13,880
Speaker 3: character nor lack of willingness to succeed, these folks get screwed.

882
00:52:14,200 --> 00:52:15,279
Speaker 3: It's the only way to say it.

883
00:52:15,680 --> 00:52:17,960
Speaker 2: And with that, we're gonna take a quick break and

884
00:52:18,160 --> 00:52:20,840
Speaker 2: come back and learn more about the messed up stuff

885
00:52:21,080 --> 00:52:22,479
Speaker 2: going on up in here.

886
00:52:27,760 --> 00:52:32,520
Speaker 3: All right, Yeah, our next beef with this well, uh well,

887
00:52:32,680 --> 00:52:35,640
Speaker 3: well it ma out. It mays out like we're just

888
00:52:35,840 --> 00:52:40,879
Speaker 3: yelling at the sky here. The systems are kind of arbitrary, right,

889
00:52:41,440 --> 00:52:42,520
Speaker 3: It's kind of silly.

890
00:52:43,200 --> 00:52:45,200
Speaker 4: Yeah, I mean yeah, I mean beyond just the lack

891
00:52:45,239 --> 00:52:49,120
Speaker 4: of transparency in what goes into that number that you

892
00:52:49,200 --> 00:52:52,359
Speaker 4: see on credit Karma. You know, between the two main

893
00:52:52,400 --> 00:52:56,040
Speaker 4: agencies that they show you, there does appear to be

894
00:52:56,160 --> 00:52:58,959
Speaker 4: a lot of kind of bobbing and weaving and sort

895
00:52:59,000 --> 00:53:01,920
Speaker 4: of improv going on behind the scenes when it comes

896
00:53:01,960 --> 00:53:05,040
Speaker 4: to the way the Big three come up with that

897
00:53:05,160 --> 00:53:09,320
Speaker 4: score and your credit worthiness. Regulatory audits have found that

898
00:53:09,400 --> 00:53:12,920
Speaker 4: there is a ton of variations scoring for it's similar

899
00:53:12,960 --> 00:53:16,880
Speaker 4: like airline prices or the way that sky miles.

900
00:53:16,719 --> 00:53:18,560
Speaker 5: Counts, you know, and when they're worth.

901
00:53:18,400 --> 00:53:20,560
Speaker 4: Like you might see one person who's got the same

902
00:53:20,560 --> 00:53:24,960
Speaker 4: exact situation, same exact number of cards, same exact mix

903
00:53:25,000 --> 00:53:28,520
Speaker 4: of accounts and types of loans and credit, and they

904
00:53:28,600 --> 00:53:30,920
Speaker 4: might have different numbers. Now I'm not saying that those

905
00:53:31,000 --> 00:53:34,840
Speaker 4: numbers would be like wildly different, but it does appear

906
00:53:35,600 --> 00:53:40,680
Speaker 4: that there isn't a whole lot of continuity between, you know, situations.

907
00:53:41,000 --> 00:53:43,439
Speaker 2: I would just say, I've personally seen that where I've

908
00:53:43,440 --> 00:53:46,520
Speaker 2: got three different reports that I kind of monitor and

909
00:53:46,680 --> 00:53:49,239
Speaker 2: they will never be the exact same. I don't know

910
00:53:49,280 --> 00:53:51,440
Speaker 2: if there's one time where they're all three the exact

911
00:53:51,440 --> 00:53:54,840
Speaker 2: same and it's all my same situation.

912
00:53:55,760 --> 00:53:59,120
Speaker 3: What do you guess send it to you know, Like,

913
00:53:59,760 --> 00:54:03,439
Speaker 3: let's extended to two people, right, two people getting their

914
00:54:03,760 --> 00:54:07,080
Speaker 3: three big three credit reports. So let's say you and

915
00:54:07,160 --> 00:54:10,040
Speaker 3: a random buddy, Like you're saying, no, you live the

916
00:54:10,160 --> 00:54:14,400
Speaker 3: exact same life. Weirdly enough, somehow, from cradle to grave,

917
00:54:14,800 --> 00:54:18,800
Speaker 3: you make all the same financial decisions at the exact

918
00:54:18,800 --> 00:54:22,279
Speaker 3: same time. Right, you have all the same inputs and outputs.

919
00:54:22,760 --> 00:54:26,759
Speaker 3: Logically you would have the same credit score. Right. Oh,

920
00:54:26,800 --> 00:54:29,719
Speaker 3: and you live in the same place for instance, Just

921
00:54:29,719 --> 00:54:32,880
Speaker 3: to make it, you know, two different people, not just

922
00:54:32,920 --> 00:54:35,600
Speaker 3: one person with three reports, two different people each with

923
00:54:35,640 --> 00:54:38,399
Speaker 3: three reports. If they're all doing the same thing, If

924
00:54:38,440 --> 00:54:40,560
Speaker 3: both these guys are doing the same thing, then they

925
00:54:40,600 --> 00:54:43,879
Speaker 3: should have the same variation. However, Dylan, can we get

926
00:54:43,880 --> 00:54:49,240
Speaker 3: a buzzer noise? Wrong? We be incorrect. This is demonstrably

927
00:54:49,680 --> 00:54:52,799
Speaker 3: not the case now, as you were saying a second ago, there, Noel,

928
00:54:54,239 --> 00:54:57,480
Speaker 3: is it a great variation? Not all the time, not

929
00:54:57,840 --> 00:55:01,399
Speaker 3: all the time. But this is why you might find

930
00:55:01,440 --> 00:55:06,120
Speaker 3: yourself doing pretty dang good with one financial info cartel,

931
00:55:06,400 --> 00:55:10,279
Speaker 3: and then less good in the opinion of another. Right

932
00:55:11,719 --> 00:55:16,040
Speaker 3: along the way, you'll find something else disturbing. Despite all

933
00:55:16,120 --> 00:55:22,560
Speaker 3: their power, despite the acquisition and deployment, big data, and

934
00:55:22,680 --> 00:55:25,399
Speaker 3: the way they lean on the government. The Big three

935
00:55:25,960 --> 00:55:30,200
Speaker 3: often still get stuff completely wrong. Let's go back to

936
00:55:30,239 --> 00:55:33,600
Speaker 3: the credit card analogy. What if we look at guys,

937
00:55:33,640 --> 00:55:37,880
Speaker 3: what were your favorite classes in uh, say high school?

938
00:55:40,239 --> 00:55:46,040
Speaker 5: Yes? History, Science, Yeah, social studies, Oh.

939
00:55:46,000 --> 00:55:49,440
Speaker 3: Right, yeah, love the studies. So let's say we've got

940
00:55:49,480 --> 00:55:56,160
Speaker 3: our protagonist here and we have each acet out use English.

941
00:55:56,440 --> 00:56:01,080
Speaker 3: So we have each aced every single quiz, every homework assignment,

942
00:56:01,560 --> 00:56:05,719
Speaker 3: every test. We know good and gosh darn well, we

943
00:56:05,760 --> 00:56:10,200
Speaker 3: got an A plus on every single possible graded thing.

944
00:56:10,760 --> 00:56:14,239
Speaker 3: But when the report card arrives, we see a big

945
00:56:14,239 --> 00:56:18,160
Speaker 3: old B minus for science, a B minus for history

946
00:56:18,200 --> 00:56:21,239
Speaker 3: of B minus for English. We have all our assignments

947
00:56:21,280 --> 00:56:23,879
Speaker 3: with us, we look back at them, we shuffle through

948
00:56:23,880 --> 00:56:26,520
Speaker 3: our papers, we looked through our quizzes and our homework

949
00:56:27,120 --> 00:56:31,040
Speaker 3: lo and behold, a plus on every single one. We say,

950
00:56:31,040 --> 00:56:34,480
Speaker 3: what happened in a classroom setting, you would go to

951
00:56:34,560 --> 00:56:37,280
Speaker 3: your teacher, and we hope that your teacher would clock

952
00:56:37,360 --> 00:56:40,000
Speaker 3: the mistake. We hope your teacher would have your back

953
00:56:40,360 --> 00:56:45,040
Speaker 3: and correct it and say, oh, Tennessee, oh, Copenhagen, Brown, Oh,

954
00:56:45,120 --> 00:56:49,080
Speaker 3: Matt Frederick, Oh, redacted. You did get an A plus.

955
00:56:49,280 --> 00:56:52,200
Speaker 3: We're correcting it. But it is not the case with

956
00:56:52,320 --> 00:56:55,120
Speaker 3: credit reports. Have you, guys ever tried to correct aid

957
00:56:55,160 --> 00:56:58,200
Speaker 3: and inaccuracy on one of the credit reports?

958
00:56:58,520 --> 00:57:02,320
Speaker 4: I mean the bureau in the name. Anytime you engage

959
00:57:02,320 --> 00:57:05,440
Speaker 4: with bureaucracy, it's not going to be any fun and

960
00:57:05,560 --> 00:57:08,279
Speaker 4: typically once again set up to fail, set up for

961
00:57:08,320 --> 00:57:11,479
Speaker 4: you to fail, because in order to actually do the thing,

962
00:57:11,680 --> 00:57:14,800
Speaker 4: to even speak to a human, which probably still wouldn't happen,

963
00:57:15,400 --> 00:57:18,680
Speaker 4: it's going to be prohibitively pedantic and obnoxious, and the

964
00:57:18,680 --> 00:57:21,440
Speaker 4: process is not going to be set up for you

965
00:57:21,520 --> 00:57:26,320
Speaker 4: to succeed. We are talking about things like personal information

966
00:57:26,480 --> 00:57:32,960
Speaker 4: that's gotten wrong, incorrect balances, and amount owed versus available credit.

967
00:57:33,160 --> 00:57:35,960
Speaker 4: We're talking about delinquent marks, which we haven't really talked

968
00:57:36,000 --> 00:57:39,160
Speaker 4: about much in the episode. The idea of being in

969
00:57:39,200 --> 00:57:43,439
Speaker 4: collections also, by the way, being in collections is such

970
00:57:43,440 --> 00:57:46,240
Speaker 4: a vague thing. I recently got a collection noticed for

971
00:57:46,280 --> 00:57:49,560
Speaker 4: a hospital bill that I had not realized wasn't paid

972
00:57:49,560 --> 00:57:51,000
Speaker 4: off for a time that my kid went to a

973
00:57:51,040 --> 00:57:54,640
Speaker 4: children's hospital many years ago, and it did not hit

974
00:57:54,680 --> 00:57:57,200
Speaker 4: my credit. I caught it in time, even though there

975
00:57:57,240 --> 00:57:59,480
Speaker 4: was a collection out on me. They literally told me

976
00:58:00,040 --> 00:58:02,439
Speaker 4: it's not going to hit your credit. The timing as

977
00:58:02,440 --> 00:58:04,600
Speaker 4: to when those things hit your credit or impact your

978
00:58:04,600 --> 00:58:08,120
Speaker 4: score super vague as well, very very very little transparency

979
00:58:08,120 --> 00:58:10,640
Speaker 4: into that, not to mention that these things can be

980
00:58:10,760 --> 00:58:14,560
Speaker 4: recorded completely in error for you, as a human being,

981
00:58:14,640 --> 00:58:16,160
Speaker 4: with very little recourse.

982
00:58:16,200 --> 00:58:19,480
Speaker 3: Or a mistaken identity. Shout out to all the John

983
00:58:19,520 --> 00:58:24,000
Speaker 3: Smiths out there. Old addresses something as simple as a

984
00:58:24,080 --> 00:58:30,200
Speaker 3: transposed number. Now in this it are high school metaphor

985
00:58:30,560 --> 00:58:34,480
Speaker 3: your teacher giving you an incorrect grade is not a person,

986
00:58:34,960 --> 00:58:39,880
Speaker 3: It's an algorithm. These are automated systems that are not consolidated.

987
00:58:40,160 --> 00:58:46,400
Speaker 3: That algorithm is woefully imperfect predicting behavior and at absorbing data.

988
00:58:46,640 --> 00:58:50,680
Speaker 3: That's why thousands of cases get all the way to

989
00:58:50,720 --> 00:58:54,280
Speaker 3: court every single year in the United States. But that

990
00:58:54,480 --> 00:58:59,600
Speaker 3: same opacity and arbitrary nature that makes these systems difficult

991
00:58:59,640 --> 00:59:03,720
Speaker 3: to part also makes them very difficult to fix. We

992
00:59:03,760 --> 00:59:07,800
Speaker 3: talked about it in the past. A company, a corporate entity,

993
00:59:07,920 --> 00:59:14,920
Speaker 3: especially these leviathans, they can afford to grind through a

994
00:59:14,960 --> 00:59:20,880
Speaker 3: courtroom dispute for years and years and years, but individuals

995
00:59:20,920 --> 00:59:25,160
Speaker 3: often can't afford to suffer through the consequences these protracted

996
00:59:25,240 --> 00:59:29,320
Speaker 3: legal battles. The game is brigged, agreed.

997
00:59:30,120 --> 00:59:34,000
Speaker 2: I'm going to list off just a number of acquisitions

998
00:59:34,040 --> 00:59:36,880
Speaker 2: that have been made by TransUnion and Equifax over the years,

999
00:59:36,960 --> 00:59:40,200
Speaker 2: and these are all companies that were attempting to automate

1000
00:59:40,360 --> 00:59:45,360
Speaker 2: certain data collection processes from different segments, from different markets.

1001
00:59:45,480 --> 00:59:49,680
Speaker 2: Here you go, Credit Vision, Smart Move, Clear, iq E

1002
00:59:49,800 --> 00:59:55,720
Speaker 2: Scan Data Systems, Trust, ev Factor, Trust Call, Credit Information Group.

1003
00:59:55,840 --> 00:59:57,560
Speaker 3: New Star, Sonick.

1004
00:59:58,360 --> 01:00:02,680
Speaker 2: Oh wait, we've got more Hell Both Checks Incorporated, Osborne Laboratories,

1005
01:00:02,720 --> 01:00:12,160
Speaker 2: Electronic Tabulating Services, Choice Point Clerdagy Electronics. You we've got

1006
01:00:12,200 --> 01:00:17,439
Speaker 2: more Authority Talks, VEDA, Reach Tell. There's so many of them,

1007
01:00:17,480 --> 01:00:20,640
Speaker 2: and they're all corporations that were acquired by these huge

1008
01:00:21,400 --> 01:00:25,520
Speaker 2: organizations corporations in order to better track you and what

1009
01:00:25,560 --> 01:00:28,160
Speaker 2: you do. But the problem that we're describing here is

1010
01:00:28,160 --> 01:00:30,960
Speaker 2: that all of them have their own little algorithms that

1011
01:00:31,000 --> 01:00:34,520
Speaker 2: they go into pulling all this data from places. If

1012
01:00:34,600 --> 01:00:37,080
Speaker 2: you look at something as simple as the white pages

1013
01:00:37,160 --> 01:00:39,440
Speaker 2: in the US, think about I don't know if any

1014
01:00:39,520 --> 01:00:42,200
Speaker 2: guys have perused yourselves, or your family or anyone on

1015
01:00:42,280 --> 01:00:43,440
Speaker 2: white pages recently.

1016
01:00:44,920 --> 01:00:46,480
Speaker 3: A lot of data is wrong.

1017
01:00:47,160 --> 01:00:49,600
Speaker 2: So much data is wrong on that platform.

1018
01:00:49,800 --> 01:00:52,440
Speaker 4: It's it's not that dissimilar from the way, Like you know,

1019
01:00:52,440 --> 01:00:55,520
Speaker 4: different social media platforms have their own algorithms. These are

1020
01:00:55,560 --> 01:00:59,440
Speaker 4: proprietary things, uh, and they will yield different results and

1021
01:00:59,520 --> 01:01:02,680
Speaker 4: analyze people's data in different ways, and then we have

1022
01:01:02,720 --> 01:01:06,280
Speaker 4: this whole concept of which one is better in this situation.

1023
01:01:06,440 --> 01:01:08,400
Speaker 4: I don't know that that's even the question we'll ask.

1024
01:01:08,880 --> 01:01:11,240
Speaker 3: It's a copy of a copy of a copy. Is

1025
01:01:11,280 --> 01:01:15,200
Speaker 3: what happens when you aggregate from aggregators, such that an

1026
01:01:15,240 --> 01:01:21,320
Speaker 3: imperfection can be like an artifact in video or audio.

1027
01:01:24,320 --> 01:01:28,800
Speaker 3: I'm starting to personify it, but it can or anthropomorphize it.

1028
01:01:28,800 --> 01:01:33,800
Speaker 3: It can expand, right, the small mistake, the small transposed number,

1029
01:01:34,080 --> 01:01:39,120
Speaker 3: the wrong John Smith Right, that information is acquired by

1030
01:01:39,240 --> 01:01:42,000
Speaker 3: a bigger and bigger fish, a bigger and bigger thing

1031
01:01:42,320 --> 01:01:48,880
Speaker 3: in the sea of communication, and eventually that growth metastasizes, right,

1032
01:01:49,000 --> 01:01:54,200
Speaker 3: with serious consequences. And this can be corrected. But the

1033
01:01:54,240 --> 01:01:57,600
Speaker 3: onus is on you as the victim, right, The onus

1034
01:01:57,680 --> 01:02:01,800
Speaker 3: is on you as the average person, as the non

1035
01:02:01,840 --> 01:02:07,120
Speaker 3: institution in the party. So you're outnumbered, you're outmanned, you're outgunned,

1036
01:02:07,400 --> 01:02:11,160
Speaker 3: and you have to go manually, right, unless you enlist

1037
01:02:11,520 --> 01:02:14,680
Speaker 3: a pretty good lawyer, you have to go manually and

1038
01:02:15,640 --> 01:02:18,600
Speaker 3: talk to the three big dogs of maybe a handful

1039
01:02:18,640 --> 01:02:22,240
Speaker 3: of others and attempt to correct this attempt to get

1040
01:02:22,280 --> 01:02:26,600
Speaker 3: an investigation. We mentioned earlier, the US federal government has

1041
01:02:26,720 --> 01:02:32,440
Speaker 3: put forward a few good faith efforts that were objectively

1042
01:02:32,520 --> 01:02:36,760
Speaker 3: kind of hindered or declaud in Congress and in action

1043
01:02:37,440 --> 01:02:42,960
Speaker 3: to make these credit bureaus investigate those shenanigans, those missteps,

1044
01:02:43,360 --> 01:02:47,600
Speaker 3: not all of which are wilful. They're just crimes of negligence.

1045
01:02:48,160 --> 01:02:53,120
Speaker 3: And that's those corrections happen if they investigate in the

1046
01:02:53,160 --> 01:02:55,600
Speaker 3: first place. And that leads us to the last point.

1047
01:02:56,440 --> 01:03:01,040
Speaker 3: These bureaus don't really have an incentive to investigate or fix.

1048
01:03:01,080 --> 01:03:05,120
Speaker 4: Errors, similar to like you know, filing a complaint with Twitter,

1049
01:03:05,480 --> 01:03:08,480
Speaker 4: or filing a complaint with Roadblocks. You know, we know

1050
01:03:08,520 --> 01:03:10,760
Speaker 4: there are all kinds of nefarious activity that goes on

1051
01:03:10,800 --> 01:03:13,920
Speaker 4: there when you're dealing with a corporation or an agency

1052
01:03:14,160 --> 01:03:15,680
Speaker 4: or an organization.

1053
01:03:15,200 --> 01:03:16,080
Speaker 5: Of that scale.

1054
01:03:16,600 --> 01:03:19,520
Speaker 4: I'm not making excuses for them, but it's difficult to

1055
01:03:19,640 --> 01:03:23,560
Speaker 4: address every single you know, million of persons. You know,

1056
01:03:23,800 --> 01:03:27,800
Speaker 4: the beef or a little discrepancy, it's difficult. I recognize that,

1057
01:03:28,000 --> 01:03:30,320
Speaker 4: you know, same like with the irs and such like.

1058
01:03:30,360 --> 01:03:33,680
Speaker 4: I can't imagine how they function these dates, but it's

1059
01:03:33,920 --> 01:03:36,360
Speaker 4: it's it's them then deciding or having some sort of

1060
01:03:36,360 --> 01:03:37,440
Speaker 4: internal threshold that.

1061
01:03:37,440 --> 01:03:40,440
Speaker 6: Itself is very opaque as.

1062
01:03:40,240 --> 01:03:43,920
Speaker 4: To what constitutes something that is worth them pursuing and

1063
01:03:43,960 --> 01:03:47,000
Speaker 4: what constitutes something that is worth them giving you a

1064
01:03:47,040 --> 01:03:51,720
Speaker 4: pat kind of you know, boiler plate generated response and

1065
01:03:51,760 --> 01:03:52,440
Speaker 4: then move on.

1066
01:03:53,240 --> 01:03:57,200
Speaker 3: Yeah, and I love that point because again the insective,

1067
01:03:58,480 --> 01:04:03,240
Speaker 3: these are for profitents, so their incentive is to maintain

1068
01:04:03,360 --> 01:04:08,640
Speaker 3: profit and to please their consumers. Their consumers again, are

1069
01:04:08,720 --> 01:04:13,200
Speaker 3: the lenders, not you. So if the lenders are happy

1070
01:04:13,680 --> 01:04:15,960
Speaker 3: with a little bit of jazz, a little bit of

1071
01:04:15,960 --> 01:04:20,960
Speaker 3: inaccuracy here and there, then jolly good ticketyboo tally ho.

1072
01:04:22,080 --> 01:04:27,640
Speaker 3: We know that big data, or the in simple terms,

1073
01:04:27,680 --> 01:04:33,560
Speaker 3: the massive hoovering of all possible information Palenteer style. We

1074
01:04:33,720 --> 01:04:37,560
Speaker 3: know that is very exciting and it has indeed been

1075
01:04:38,040 --> 01:04:43,200
Speaker 3: positive or pitched as a solution to improving accuracy at

1076
01:04:43,240 --> 01:04:46,880
Speaker 3: the root from the jump day one stuff. But don't

1077
01:04:46,880 --> 01:04:50,040
Speaker 3: get us started on big data, folks. Just check out

1078
01:04:50,120 --> 01:04:55,600
Speaker 3: our earlier episodes and wish us luck crossing international boarders,

1079
01:04:55,920 --> 01:04:56,800
Speaker 3: we're doing this show.

1080
01:04:57,160 --> 01:04:59,800
Speaker 2: Well, sorry, guys, I had a little mind to burp

1081
01:05:00,000 --> 01:05:06,240
Speaker 2: where I got interested in the shareholders of something like TransUnion,

1082
01:05:06,240 --> 01:05:09,240
Speaker 2: which is a publicly traded company, and I just noticed

1083
01:05:09,320 --> 01:05:12,040
Speaker 2: there's some there's some folks in here that we've talked

1084
01:05:12,040 --> 01:05:15,880
Speaker 2: about before. Let's see number one at the top, according

1085
01:05:15,920 --> 01:05:20,040
Speaker 2: to Yahoo Finance, with nineteen point one million shares, a

1086
01:05:20,040 --> 01:05:24,720
Speaker 2: little group called black Rock Incorporated. Just below them with

1087
01:05:24,920 --> 01:05:29,800
Speaker 2: nineteen point zero six million shares, Vanguard Group Incorporated, two

1088
01:05:29,880 --> 01:05:33,240
Speaker 2: of the major investment firms that we've talked about before.

1089
01:05:33,080 --> 01:05:33,439
Speaker 3: In the past.

1090
01:05:33,480 --> 01:05:36,720
Speaker 2: It's just interesting that it's all interconnected in that way,

1091
01:05:36,800 --> 01:05:41,040
Speaker 2: isn't it. The folks that get to decide how good

1092
01:05:41,080 --> 01:05:43,280
Speaker 2: you are at having credit and how worthy.

1093
01:05:43,400 --> 01:05:45,320
Speaker 5: So the's slending you the money.

1094
01:05:45,320 --> 01:05:48,680
Speaker 2: And that apartment that you're trying to get is owned.

1095
01:05:48,760 --> 01:05:54,480
Speaker 6: But it's the same hand, the same buppet Weird Is

1096
01:05:54,520 --> 01:05:54,960
Speaker 6: it weird?

1097
01:05:55,000 --> 01:05:55,200
Speaker 3: Though?

1098
01:05:55,240 --> 01:05:55,840
Speaker 5: I think it is.

1099
01:05:55,920 --> 01:05:58,560
Speaker 6: It makes absolute sense. It is utterly by.

1100
01:05:58,440 --> 01:06:02,240
Speaker 4: Design and isn't particularly surprising at all, Like we just

1101
01:06:02,440 --> 01:06:03,560
Speaker 4: you know, it's like.

1102
01:06:05,240 --> 01:06:06,680
Speaker 5: It's frustrating, is what it is.

1103
01:06:07,520 --> 01:06:13,040
Speaker 3: It's yikes, yike's indeed, And there are questions about solutions. Right,

1104
01:06:13,680 --> 01:06:16,080
Speaker 3: there are a lot of cases we probably won't get

1105
01:06:16,120 --> 01:06:20,320
Speaker 3: into this evening. We do want to shout out something

1106
01:06:20,400 --> 01:06:25,360
Speaker 3: called the Consumer Financial Protection Bureau, or CFB. In January

1107
01:06:25,400 --> 01:06:28,680
Speaker 3: of last year, the CFPB sued the heck out of

1108
01:06:28,960 --> 01:06:34,480
Speaker 3: our good friends at Experience for quote unlawfully failing to

1109
01:06:34,720 --> 01:06:39,520
Speaker 3: properly investigate consumer disputes, and you can read their full

1110
01:06:39,640 --> 01:06:43,200
Speaker 3: statement online. And Matt to your point, there are a

1111
01:06:43,200 --> 01:06:47,200
Speaker 3: lot of powerful people with their fingers in the pies

1112
01:06:47,600 --> 01:06:50,160
Speaker 3: of these financial entities.

1113
01:06:50,160 --> 01:06:53,520
Speaker 4: Sure tracks that they wouldn't you know, follow those threads

1114
01:06:53,520 --> 01:06:56,360
Speaker 4: because it doesn't particularly benefit them or their lenders, who

1115
01:06:56,360 --> 01:06:59,640
Speaker 4: are their clients. What came of some of those like

1116
01:06:59,840 --> 01:07:01,480
Speaker 4: are are they being taken to task or are we

1117
01:07:01,520 --> 01:07:02,880
Speaker 4: talking risk slappy type.

1118
01:07:02,680 --> 01:07:05,120
Speaker 5: Stuff, which is of course what you would imagine.

1119
01:07:05,520 --> 01:07:08,720
Speaker 2: A couple, you know, a couple dozen million dollars and

1120
01:07:08,760 --> 01:07:09,840
Speaker 2: they're like, oh, no.

1121
01:07:11,960 --> 01:07:14,000
Speaker 6: Being a drop in the bucket, is what you're saying exactly.

1122
01:07:14,320 --> 01:07:19,240
Speaker 3: Yeah. Yeah. There was a lot of cautious optimism, hopefulness

1123
01:07:19,920 --> 01:07:22,280
Speaker 3: that there could be a real dialogue that could trigger

1124
01:07:22,320 --> 01:07:27,000
Speaker 3: a sea change, you know, maybe help prepare broken system.

1125
01:07:27,520 --> 01:07:31,280
Speaker 3: The case was dismissed in August of that same year

1126
01:07:31,920 --> 01:07:36,400
Speaker 3: tracks not even not even a slap.

1127
01:07:36,480 --> 01:07:40,160
Speaker 4: That's just just just a outright let's move on, guys,

1128
01:07:40,160 --> 01:07:40,959
Speaker 4: nothing to see here.

1129
01:07:41,600 --> 01:07:47,080
Speaker 3: Whoo. Yeah, the judge, Michelle Williams in the Central District

1130
01:07:47,160 --> 01:07:51,960
Speaker 3: of California did dismiss the suit without prejudice, which is

1131
01:07:53,480 --> 01:07:57,000
Speaker 3: not the worst outcome because it means that our friends

1132
01:07:57,040 --> 01:08:02,240
Speaker 3: at the Consumer Financial Protection Bureau, despite massive budget cuts,

1133
01:08:02,960 --> 01:08:06,560
Speaker 3: can file a complaint in the future. Who knows, maybe.

1134
01:08:06,280 --> 01:08:09,520
Speaker 4: It'll work out wonderful, the right to complain. Thank you

1135
01:08:09,640 --> 01:08:14,280
Speaker 4: so much for that, blessing, benevolent financial overlords.

1136
01:08:15,480 --> 01:08:19,040
Speaker 3: Oh dear, So what do you think, guys? Are there solutions?

1137
01:08:19,680 --> 01:08:24,479
Speaker 3: Have we done an okay job outlining the credit bureau conspiracy.

1138
01:08:24,160 --> 01:08:27,160
Speaker 5: Of how ft it is? Yeah? I think so. I

1139
01:08:27,200 --> 01:08:27,559
Speaker 5: think so.

1140
01:08:27,760 --> 01:08:30,360
Speaker 2: I think we've done an awesome job. It does go deeper, though,

1141
01:08:30,520 --> 01:08:32,759
Speaker 2: and it goes into the things we've already talked about.

1142
01:08:32,960 --> 01:08:36,680
Speaker 2: We just we didn't dig down into them in the

1143
01:08:36,680 --> 01:08:38,600
Speaker 2: way I think we could in a future episode. And

1144
01:08:38,640 --> 01:08:41,760
Speaker 2: it's about the security state and the surveillance state thing

1145
01:08:42,120 --> 01:08:44,880
Speaker 2: that you know was growing and we were all warned

1146
01:08:44,920 --> 01:08:46,479
Speaker 2: about over and over and over again.

1147
01:08:47,080 --> 01:08:50,240
Speaker 4: Not to mention, we didn't get into how interest rates

1148
01:08:50,240 --> 01:08:53,519
Speaker 4: are set, and like the FED and the dispute going

1149
01:08:53,560 --> 01:08:57,559
Speaker 4: on between the administration of the presidential administration and the

1150
01:08:57,600 --> 01:09:00,280
Speaker 4: head of the FED, who is claiming that he's being

1151
01:09:00,320 --> 01:09:05,840
Speaker 4: targeted for not doing the bidding financially. Speaking of the president, MMM,

1152
01:09:06,320 --> 01:09:08,040
Speaker 4: whether you agree with that or not, that is what

1153
01:09:08,080 --> 01:09:12,040
Speaker 4: he is asserting. Jerome Powell, the head of the Federal.

1154
01:09:11,760 --> 01:09:15,439
Speaker 2: Reserve, really as the financial systems really are all connected.

1155
01:09:16,400 --> 01:09:19,080
Speaker 2: And the creepy thing to me is how all of

1156
01:09:19,120 --> 01:09:21,040
Speaker 2: that goes back into just how much they know about

1157
01:09:21,120 --> 01:09:25,320
Speaker 2: us individually and how that was kind of how it

1158
01:09:25,360 --> 01:09:27,479
Speaker 2: was supposed to be from the jump, like they were

1159
01:09:27,479 --> 01:09:30,519
Speaker 2: just supposed to know about us and everything that we did,

1160
01:09:30,600 --> 01:09:33,799
Speaker 2: so they can kind of keep tabs on us. Maybe

1161
01:09:34,439 --> 01:09:35,719
Speaker 2: maybe not, Maybe that's too.

1162
01:09:35,680 --> 01:09:38,479
Speaker 5: Much, too far of a lost I can tell you that.

1163
01:09:38,600 --> 01:09:42,720
Speaker 2: Over the Oxford Research Encyclopedias, Josh Lower in November of

1164
01:09:42,800 --> 01:09:45,879
Speaker 2: twenty twenty two talked about the connection between the surveillance

1165
01:09:45,920 --> 01:09:48,840
Speaker 2: state and credit reporting agencies. You can read it. It's

1166
01:09:48,840 --> 01:09:52,120
Speaker 2: titled Credit Reporting and the History of Commercial Surveillance in America.

1167
01:09:52,760 --> 01:09:54,360
Speaker 2: I think you do have to have a subscription to

1168
01:09:56,120 --> 01:09:58,719
Speaker 2: something some version of the thing in order to see

1169
01:09:58,760 --> 01:09:59,439
Speaker 2: all of his writing.

1170
01:09:59,479 --> 01:10:00,479
Speaker 5: But it is.

1171
01:10:00,439 --> 01:10:03,479
Speaker 2: Fascinating linking stuff all the way back to the Civil

1172
01:10:03,520 --> 01:10:07,200
Speaker 2: War and before to the modern twenty first century credit

1173
01:10:07,240 --> 01:10:08,439
Speaker 2: reporting data collection.

1174
01:10:09,000 --> 01:10:10,719
Speaker 4: Take me back to the Stone Age. I'll take clay

1175
01:10:10,720 --> 01:10:12,920
Speaker 4: tablets over this stuff any day of the week.

1176
01:10:13,320 --> 01:10:16,479
Speaker 3: Speaking a linear time. Who knows. Maybe in the future

1177
01:10:16,640 --> 01:10:20,360
Speaker 3: we will see some genuine solutions right to the stuff

1178
01:10:20,640 --> 01:10:23,479
Speaker 3: we explore tonight. We do a lot of research for

1179
01:10:23,520 --> 01:10:26,840
Speaker 3: these things. We never write a script. We explore it

1180
01:10:26,960 --> 01:10:31,720
Speaker 3: organically together. Maybe we'll see accountability for data providers at

1181
01:10:31,760 --> 01:10:35,719
Speaker 3: every level to avoid those problems that magnify over time.

1182
01:10:36,160 --> 01:10:41,000
Speaker 3: Maybe we'll see proactive free access, meaning that it's on

1183
01:10:41,200 --> 01:10:45,120
Speaker 3: the big dogs, the big badgers, to send you a

1184
01:10:45,200 --> 01:10:49,280
Speaker 3: credit report without you manually requesting it, or going through

1185
01:10:49,320 --> 01:10:52,479
Speaker 3: an app which also harvests the data on your phone,

1186
01:10:53,000 --> 01:10:57,160
Speaker 3: or you know, maybe just better accuracy of information in

1187
01:10:57,240 --> 01:11:00,519
Speaker 3: the first place. Guys, I wanted to ask group a

1188
01:11:00,680 --> 01:11:06,200
Speaker 3: question about the argument of personal responsibility right, the argument

1189
01:11:06,280 --> 01:11:10,360
Speaker 3: that if you play the game right, you will naturally prosper.

1190
01:11:10,800 --> 01:11:14,639
Speaker 3: What do we think about that conversation the winter.

1191
01:11:15,360 --> 01:11:17,280
Speaker 4: I mean, it's just so hard to feel that you

1192
01:11:17,280 --> 01:11:21,000
Speaker 4: can achieve that without just some significant windfall or bit

1193
01:11:21,040 --> 01:11:23,920
Speaker 4: of luck or being born on third base. You know,

1194
01:11:25,080 --> 01:11:28,280
Speaker 4: maybe I'm cynical, or maybe you know, the state of

1195
01:11:28,320 --> 01:11:31,360
Speaker 4: the world has put me in this mindset. I think

1196
01:11:31,400 --> 01:11:33,840
Speaker 4: you can. Definitely, it's certainly possible to work yourself up

1197
01:11:33,880 --> 01:11:36,000
Speaker 4: from the ground up and become successful, But it just

1198
01:11:36,040 --> 01:11:38,960
Speaker 4: seems more and more like the deck is way stacked

1199
01:11:39,000 --> 01:11:40,360
Speaker 4: against us in that respect.

1200
01:11:40,800 --> 01:11:43,400
Speaker 2: There are all these commercial entities out there pushing apps

1201
01:11:43,400 --> 01:11:45,960
Speaker 2: and services that can increase your credit score by like

1202
01:11:46,120 --> 01:11:48,559
Speaker 2: twenty points, you know, and that's a big win, and

1203
01:11:48,600 --> 01:11:51,439
Speaker 2: it could be a big win for somebody, But how

1204
01:11:51,479 --> 01:11:54,360
Speaker 2: big of a change is twenty points? Actually, if you

1205
01:11:54,439 --> 01:11:56,760
Speaker 2: look at the difference between you know, someone in the

1206
01:11:56,800 --> 01:11:59,600
Speaker 2: six hundreds versus someone in the eight hundreds, and how

1207
01:11:59,760 --> 01:12:02,519
Speaker 2: you know, it feels like you'd have to win the

1208
01:12:02,560 --> 01:12:04,880
Speaker 2: lottery to be able to pay off all of your

1209
01:12:04,960 --> 01:12:07,920
Speaker 2: existing debts and then do a bunch of other stuff

1210
01:12:07,960 --> 01:12:11,519
Speaker 2: with money before you could go from the five hundred

1211
01:12:11,520 --> 01:12:13,840
Speaker 2: and six hundreds all the way up. It feels like that,

1212
01:12:13,960 --> 01:12:16,880
Speaker 2: And maybe that's wrong. Maybe we just haven't seen enough

1213
01:12:16,880 --> 01:12:19,200
Speaker 2: examples of that happening in the news or something.

1214
01:12:19,680 --> 01:12:22,679
Speaker 4: I mean, twenty points, you could see your credit dipping

1215
01:12:22,720 --> 01:12:25,519
Speaker 4: twenty points from one of those hard inquiries. You could

1216
01:12:25,520 --> 01:12:28,240
Speaker 4: see your credit score dipping twenty points just from one

1217
01:12:28,320 --> 01:12:31,160
Speaker 4: delinquent payment. You could see your credit score dipping twenty

1218
01:12:31,200 --> 01:12:35,000
Speaker 4: points from maybe closing one older account, which I think

1219
01:12:35,040 --> 01:12:37,000
Speaker 4: we didn't maybe talk about on MIC, but definitely, if

1220
01:12:37,000 --> 01:12:38,679
Speaker 4: you're going to close accounts, don't close.

1221
01:12:38,479 --> 01:12:39,080
Speaker 5: Your oldest one.

1222
01:12:39,160 --> 01:12:42,000
Speaker 4: That's the one that's establishing your credit age and a

1223
01:12:42,120 --> 01:12:44,400
Speaker 4: very big factor in that number.

1224
01:12:44,800 --> 01:12:45,960
Speaker 2: It's just upsetting you guys.

1225
01:12:46,520 --> 01:12:50,599
Speaker 3: Yeah, agreed. I mean, I think we can all agree,

1226
01:12:51,040 --> 01:12:56,360
Speaker 3: after exploring this together, that the system, if not broken,

1227
01:12:57,240 --> 01:13:00,400
Speaker 3: is pretty rigged. You know. It reminds me that joke

1228
01:13:00,479 --> 01:13:04,240
Speaker 3: in the Wild West where a guy is playing a

1229
01:13:04,520 --> 01:13:08,599
Speaker 3: clearly rigged game of poker in an old timey saloon.

1230
01:13:09,160 --> 01:13:11,559
Speaker 3: Another guy walks in and says, hey, you know this guy,

1231
01:13:11,800 --> 01:13:14,080
Speaker 3: this game is rigged, right, And the guy in the

1232
01:13:14,120 --> 01:13:17,519
Speaker 3: saloon says, I do, but it's the only game in town.

1233
01:13:18,120 --> 01:13:21,600
Speaker 3: So maybe there should be a different game. Maybe we

1234
01:13:21,640 --> 01:13:24,520
Speaker 3: should go for a system that is not so obtuse,

1235
01:13:24,960 --> 01:13:29,280
Speaker 3: so full of bullying, or riddled with profit seeking middleman.

1236
01:13:29,680 --> 01:13:32,080
Speaker 3: For now, it does seem fair to say this is

1237
01:13:32,160 --> 01:13:34,519
Speaker 3: chock full of the stuff they don't want you to know.

1238
01:13:35,000 --> 01:13:39,760
Speaker 4: Well, it's interesting because these are public, publicly traded, for

1239
01:13:39,920 --> 01:13:43,439
Speaker 4: profit institutions, and I guess it feels like that they're

1240
01:13:43,439 --> 01:13:46,200
Speaker 4: all in cohoots with one another to some degree. And

1241
01:13:46,560 --> 01:13:49,360
Speaker 4: the argument that it's not a monopoly is because they

1242
01:13:49,400 --> 01:13:52,479
Speaker 4: are like a handful of them. But it sure feels

1243
01:13:52,520 --> 01:13:55,000
Speaker 4: like a monopoly because the system is the thing that

1244
01:13:55,040 --> 01:13:57,880
Speaker 4: doesn't change that you have no other choice but to

1245
01:13:57,920 --> 01:14:01,479
Speaker 4: participate in this credit system, So that feels a bit

1246
01:14:01,520 --> 01:14:02,560
Speaker 4: like a monopoly to me.

1247
01:14:02,720 --> 01:14:05,320
Speaker 2: I don't know, because we didn't talk about the visa

1248
01:14:05,360 --> 01:14:08,519
Speaker 2: and MasterCard of it all, right, and how all of

1249
01:14:08,560 --> 01:14:11,240
Speaker 2: this stuff, all roads and all credit cards and all

1250
01:14:11,240 --> 01:14:15,439
Speaker 2: credit reporting agencies all lead back to the same issuing companies, right,

1251
01:14:15,800 --> 01:14:18,400
Speaker 2: and then how every one of those transactions can be

1252
01:14:18,520 --> 01:14:23,400
Speaker 2: traced and tracked and calculated as to what that means

1253
01:14:23,400 --> 01:14:25,960
Speaker 2: for your personal score and also the score of wherever

1254
01:14:26,000 --> 01:14:28,439
Speaker 2: you live and the score of people in your same

1255
01:14:28,479 --> 01:14:31,719
Speaker 2: demographic and it's all connected.

1256
01:14:32,200 --> 01:14:34,840
Speaker 4: It's kind of like stats for cars and Mario Kart,

1257
01:14:34,840 --> 01:14:36,639
Speaker 4: Like when you look at them on the screen, they're

1258
01:14:36,640 --> 01:14:38,839
Speaker 4: one thing, but there's actually a lot of secret stats

1259
01:14:39,040 --> 01:14:41,320
Speaker 4: that you actually have to drive on certain surfaces to

1260
01:14:41,360 --> 01:14:43,960
Speaker 4: figure out what those are because they're not represented in

1261
01:14:43,960 --> 01:14:46,840
Speaker 4: the numbers that you see in front of your eyeballs.

1262
01:14:47,160 --> 01:14:51,760
Speaker 3: We also didn't talk about the aggressive marketing campaigns and

1263
01:14:51,800 --> 01:14:56,040
Speaker 3: the aggressive political lobbying that the Big three Badgers use

1264
01:14:56,240 --> 01:14:59,559
Speaker 3: to put themselves in the catbird seat in the first place.

1265
01:15:00,120 --> 01:15:04,559
Speaker 3: There's clearly more to get into here, and we hope

1266
01:15:04,560 --> 01:15:07,200
Speaker 3: that we hope that this is one of those episodes

1267
01:15:07,240 --> 01:15:12,559
Speaker 3: that might have sounded deceptively like a snoozefest, but really

1268
01:15:12,640 --> 01:15:17,040
Speaker 3: hits on some genuine, true conspiracy, some of which may

1269
01:15:17,080 --> 01:15:19,639
Speaker 3: not be intentional. We would love to hear your thoughts.

1270
01:15:19,760 --> 01:15:22,439
Speaker 3: You can find us on lie. You can call us

1271
01:15:22,479 --> 01:15:24,920
Speaker 3: on a phone. You can always send us an email

1272
01:15:25,240 --> 01:15:25,719
Speaker 3: mm HM.

1273
01:15:25,720 --> 01:15:28,519
Speaker 4: Can find us on your social media platform of choice

1274
01:15:28,560 --> 01:15:32,280
Speaker 4: at the handle conspiracy stuff or Conspiracy Stuff Show, depending,

1275
01:15:32,439 --> 01:15:34,840
Speaker 4: and you can also give us a telephone call.

1276
01:15:35,520 --> 01:15:38,719
Speaker 2: We have a number. It is one eight three three

1277
01:15:38,760 --> 01:15:42,600
Speaker 2: STDWYITK use one of these, probably one that doesn't have

1278
01:15:42,640 --> 01:15:43,480
Speaker 2: a shattered.

1279
01:15:43,160 --> 01:15:45,880
Speaker 6: Screen and call it.

1280
01:15:46,080 --> 01:15:48,400
Speaker 2: You will get our voicemail and you will be able

1281
01:15:48,439 --> 01:15:51,080
Speaker 2: to talk for about three minutes. Give yourself a cool

1282
01:15:51,160 --> 01:15:52,479
Speaker 2: nickname and let us know if we can use any

1283
01:15:52,560 --> 01:15:54,840
Speaker 2: message on the air. If you would like to send

1284
01:15:54,920 --> 01:15:56,200
Speaker 2: us an email, you can do that too.

1285
01:15:56,280 --> 01:16:00,519
Speaker 3: We are the entity's the read each piece of correspondence receive.

1286
01:16:00,800 --> 01:16:04,680
Speaker 3: Let's hang out here in the dark conspiracy at iHeartRadio

1287
01:16:04,880 --> 01:16:23,479
Speaker 3: dot com.

1288
01:16:23,640 --> 01:16:25,679
Speaker 2: Stuff they don't want you to know is a production

1289
01:16:25,800 --> 01:16:30,320
Speaker 2: of iHeartRadio. For more podcasts from iHeartRadio, visit the iHeartRadio app,

1290
01:16:30,400 --> 01:16:33,640
Speaker 2: Apple Podcasts, or wherever you listen to your favorite shows.