1 00:00:00,240 --> 00:00:27,680 Speaker 1: Ridiculous History is a production of iHeartRadio. Welcome back to 2 00:00:27,720 --> 00:00:30,520 Speaker 1: the show Ridiculous Historians. Thank you, as always so much 3 00:00:30,560 --> 00:00:34,760 Speaker 1: for tuning in. Please pardon my slightly batman voice, little 4 00:00:34,920 --> 00:00:37,680 Speaker 1: little bit of weather on top of me, but we 5 00:00:37,880 --> 00:00:41,760 Speaker 1: wanted to bring this episode to you. Guys were very 6 00:00:41,760 --> 00:00:46,320 Speaker 1: excited about this and also, if being honest, thoroughly confused 7 00:00:46,479 --> 00:00:50,919 Speaker 1: like most of America. We were talking off air when 8 00:00:50,960 --> 00:00:53,400 Speaker 1: we did a history of credit cards, I believe it was, 9 00:00:53,479 --> 00:00:59,200 Speaker 1: and our super producer, mister Max Williams sound cute, and you, 10 00:00:59,440 --> 00:01:02,120 Speaker 1: mister Nole Brown, and I've been bowling. 11 00:01:02,240 --> 00:01:03,840 Speaker 2: We started. 12 00:01:04,240 --> 00:01:07,119 Speaker 1: We started saying, Hey, you know what's more confusing than 13 00:01:07,120 --> 00:01:10,480 Speaker 1: the idea of credit credit cards. It's the idea of 14 00:01:10,520 --> 00:01:13,920 Speaker 1: credit scores. Yeah, it does kind of have a language 15 00:01:13,959 --> 00:01:15,800 Speaker 1: all its own. The idea of like. 16 00:01:15,840 --> 00:01:20,480 Speaker 3: Hard versus soft credit inquiries, and like the two different 17 00:01:20,560 --> 00:01:27,440 Speaker 3: reporting agencies equifacts versus TransUnion and experience experience exactly, and 18 00:01:27,520 --> 00:01:30,280 Speaker 3: the idea that like, if you check your credit score, 19 00:01:30,440 --> 00:01:34,520 Speaker 3: it gives your credit score a negative impact unless you 20 00:01:34,640 --> 00:01:37,920 Speaker 3: use credit karma, which I guess is a soft inquiry. 21 00:01:38,319 --> 00:01:40,839 Speaker 2: Yes, we're gonna find out about it. It depends on how 22 00:01:40,920 --> 00:01:41,840 Speaker 2: you check the score. 23 00:01:43,280 --> 00:01:45,720 Speaker 1: It comes up every time you need a big purchase, 24 00:01:45,760 --> 00:01:50,640 Speaker 1: a house, a car, credit card, a line of a loan, 25 00:01:50,920 --> 00:01:54,560 Speaker 1: right taking out a loan. If you've ever interacted with 26 00:01:54,680 --> 00:01:57,320 Speaker 1: the financial system in the United States, folks, you would 27 00:01:57,360 --> 00:02:01,760 Speaker 1: run into credit scores and arcane and omiration of numbers 28 00:02:01,800 --> 00:02:08,080 Speaker 1: that are at times purposely obtuse, but still wield tremendous 29 00:02:08,120 --> 00:02:11,320 Speaker 1: influence on the day to day lives of average Americans. 30 00:02:11,520 --> 00:02:13,919 Speaker 1: Like do you not have enough credit? That's a ding 31 00:02:14,000 --> 00:02:16,880 Speaker 1: on your score? Do you have too much debt that's 32 00:02:16,880 --> 00:02:19,240 Speaker 1: a ding on your score? Did you get a new 33 00:02:19,320 --> 00:02:21,919 Speaker 1: credit card that has a hard inquiry like you were 34 00:02:21,919 --> 00:02:26,000 Speaker 1: talking about, Noel, that's a ding on your score. It's yeah, 35 00:02:26,040 --> 00:02:27,160 Speaker 1: it's a weird system. 36 00:02:27,480 --> 00:02:28,440 Speaker 2: To jump in here quick. 37 00:02:28,560 --> 00:02:30,959 Speaker 4: Actually, I lived with a good friend of mine, his 38 00:02:31,080 --> 00:02:35,040 Speaker 4: name's Phil, number of years back, and very fiscally responsible 39 00:02:35,120 --> 00:02:37,520 Speaker 4: dude as money saves money, stuff like that. But we 40 00:02:37,560 --> 00:02:39,840 Speaker 4: went to get in an apartment and the problem that he 41 00:02:39,880 --> 00:02:42,240 Speaker 4: had never had a credit card. This guy who's like, 42 00:02:42,320 --> 00:02:44,960 Speaker 4: never believed in debt. He doesn't just he doesn't do it. 43 00:02:45,000 --> 00:02:47,399 Speaker 4: And it's just they're like, well, we can't really give 44 00:02:47,440 --> 00:02:48,959 Speaker 4: you this apartment. Without the score, and he goes, what, 45 00:02:49,080 --> 00:02:50,880 Speaker 4: so I have to like put myself in debt to 46 00:02:50,919 --> 00:02:53,560 Speaker 4: get an apartment. Thankfully they were able to just use 47 00:02:53,560 --> 00:02:55,240 Speaker 4: my score to get the apartment, but still it was 48 00:02:55,240 --> 00:02:57,160 Speaker 4: like it's it's like. 49 00:02:57,160 --> 00:02:58,760 Speaker 2: What why are we here? 50 00:02:58,960 --> 00:03:02,799 Speaker 1: You get punished for not participating in the system. It's 51 00:03:02,840 --> 00:03:10,040 Speaker 1: a very lucrative arrangement for credit institutions and financial institutions. 52 00:03:10,320 --> 00:03:14,040 Speaker 1: And we'll see that there's a very valid arguments necessary evil. 53 00:03:14,080 --> 00:03:17,360 Speaker 1: But maybe before we really get into it, can we 54 00:03:18,160 --> 00:03:20,200 Speaker 1: talk about what a credit score is. 55 00:03:20,240 --> 00:03:22,280 Speaker 2: What do we mean when we say credit score? 56 00:03:29,000 --> 00:03:33,040 Speaker 3: Yeah, well, your credit score is a three digit number 57 00:03:33,520 --> 00:03:39,240 Speaker 3: that is a representation of your fiscal responsibility. Basically, like 58 00:03:39,280 --> 00:03:41,560 Speaker 3: you said, Ben, there's a lot of factors. And I 59 00:03:41,600 --> 00:03:45,600 Speaker 3: can't recommend credit Karma enough upfront because it does break 60 00:03:45,640 --> 00:03:47,840 Speaker 3: down a lot of the stuff for you with no 61 00:03:48,000 --> 00:03:50,280 Speaker 3: negative impact for checking it out. 62 00:03:50,320 --> 00:03:51,680 Speaker 2: It also can connect you with. 63 00:03:52,600 --> 00:03:55,360 Speaker 3: Cards and loan types and lines of credit that you 64 00:03:55,480 --> 00:03:59,600 Speaker 3: might qualify for based on your credit score. You know, 65 00:03:59,680 --> 00:04:04,280 Speaker 3: it can go from zero to I believe around nine 66 00:04:04,400 --> 00:04:07,200 Speaker 3: hundred if I'm not mistaken, Ben, I think that's the 67 00:04:07,240 --> 00:04:10,640 Speaker 3: maximum I think a good credit score would be considered 68 00:04:10,680 --> 00:04:14,880 Speaker 3: somewhere between six to eight hundred, like eight hundred being excellent. 69 00:04:15,200 --> 00:04:17,839 Speaker 3: I think even six fifty six seventy seven hundred is 70 00:04:17,839 --> 00:04:18,679 Speaker 3: considered excellent. 71 00:04:18,720 --> 00:04:21,160 Speaker 2: Once you get in this seven hundreds, you're you're doing 72 00:04:21,200 --> 00:04:21,680 Speaker 2: really well. 73 00:04:21,920 --> 00:04:24,880 Speaker 4: I feel like it's weird where it's it's like in 74 00:04:24,920 --> 00:04:27,039 Speaker 4: the seven hundreds is where there's like a lot of 75 00:04:27,200 --> 00:04:30,480 Speaker 4: change in it. So like upper seven hundreds is like excellent. Well, 76 00:04:30,520 --> 00:04:32,320 Speaker 4: lower seven hundreds is like fair. 77 00:04:32,360 --> 00:04:32,880 Speaker 2: That's right. 78 00:04:33,160 --> 00:04:35,599 Speaker 1: Also, that's where you see a lot of fluctuation too, 79 00:04:35,760 --> 00:04:39,960 Speaker 1: because something as small as a like let's say you 80 00:04:40,000 --> 00:04:43,960 Speaker 1: have an overdue payment that you missed not because you 81 00:04:44,200 --> 00:04:47,440 Speaker 1: didn't have the money, but because you somehow lost track 82 00:04:47,520 --> 00:04:49,799 Speaker 1: of something or got forgotten a dino. 83 00:04:50,040 --> 00:04:52,600 Speaker 2: Yeah, right, And then. 84 00:04:52,760 --> 00:04:58,680 Speaker 1: You can, after having spent so long accreting positive points, 85 00:04:59,040 --> 00:05:02,039 Speaker 1: then all of a sudden you're down like thirty points. 86 00:05:02,520 --> 00:05:06,240 Speaker 1: And it takes a long so you the mistakes or 87 00:05:06,279 --> 00:05:12,520 Speaker 1: the missteps have a greater immediate weight than the success 88 00:05:12,920 --> 00:05:16,440 Speaker 1: or the track record of good financial behavior. And that 89 00:05:16,440 --> 00:05:19,800 Speaker 1: that is something that has long been a subject of 90 00:05:19,839 --> 00:05:23,480 Speaker 1: criticism amid the public because you know, we can understand 91 00:05:23,520 --> 00:05:26,200 Speaker 1: that would make somebody feel like the game is rigged. Well. 92 00:05:26,240 --> 00:05:28,360 Speaker 3: It's interesting too, because one thing that can have a 93 00:05:28,400 --> 00:05:31,279 Speaker 3: really positive impact on your credit score, but also you 94 00:05:31,360 --> 00:05:33,839 Speaker 3: have to be careful, is paying off a bunch of stuff. 95 00:05:34,200 --> 00:05:36,560 Speaker 3: But if you pay off a bunch of stuff, then 96 00:05:36,600 --> 00:05:40,000 Speaker 3: you're no longer utilizing your credit, right, that can have 97 00:05:40,120 --> 00:05:44,440 Speaker 3: a negative impact on your credit score, which makes very little. 98 00:05:44,160 --> 00:05:47,560 Speaker 2: Sense, you know, from a I don't know reasoning kind 99 00:05:47,600 --> 00:05:48,320 Speaker 2: of standpoint. 100 00:05:48,760 --> 00:05:50,480 Speaker 4: And then it gets so much worse if you try 101 00:05:50,520 --> 00:05:51,919 Speaker 4: to close a credit card out. 102 00:05:52,120 --> 00:05:54,640 Speaker 2: Because that shortens your history of credit. 103 00:05:54,800 --> 00:05:57,479 Speaker 3: If then God forbid you close a card that you've 104 00:05:57,480 --> 00:06:00,240 Speaker 3: had for the longest, that all of a sudden, not 105 00:06:00,279 --> 00:06:03,800 Speaker 3: only are you likely to have a high limit on 106 00:06:03,880 --> 00:06:06,520 Speaker 3: a card you've carried for the longest, but that also 107 00:06:06,800 --> 00:06:11,800 Speaker 3: represents a large portion of your credit history right and 108 00:06:11,800 --> 00:06:12,960 Speaker 3: an age. 109 00:06:12,839 --> 00:06:17,800 Speaker 1: And it changes your debt to credit ratio. It's weird. 110 00:06:17,920 --> 00:06:21,680 Speaker 1: I mean, I can't be the only one amid us 111 00:06:21,680 --> 00:06:24,160 Speaker 1: in our fellow ridiculous of storage. As a couple of 112 00:06:24,240 --> 00:06:27,560 Speaker 1: zombie credit cards. I've paid them off, I never touched them. 113 00:06:28,279 --> 00:06:31,200 Speaker 1: Every blue moon, i'll get a letter from one of 114 00:06:31,240 --> 00:06:33,760 Speaker 1: those companies that say, hey, you have to use this 115 00:06:33,800 --> 00:06:37,000 Speaker 1: for something or we're going to close it on your behalf, 116 00:06:37,320 --> 00:06:40,040 Speaker 1: and so I will go, you know, I will go 117 00:06:40,240 --> 00:06:44,600 Speaker 1: make the most ridiculous purchase and then I will aggressively 118 00:06:45,560 --> 00:06:50,680 Speaker 1: immediately pay it off. It's such a surreal it's a 119 00:06:50,720 --> 00:06:52,120 Speaker 1: black comedy situation. 120 00:06:52,640 --> 00:06:55,360 Speaker 3: I recently had a credit account closed, and it was 121 00:06:55,400 --> 00:06:58,240 Speaker 3: a Bank of America card, And my oldest credit card 122 00:06:58,279 --> 00:07:00,159 Speaker 3: account that I've had since I was like in my 123 00:07:00,279 --> 00:07:03,359 Speaker 3: late teens early twenties was a Bank of America cards. 124 00:07:03,360 --> 00:07:06,080 Speaker 3: So when I saw that, I freaked out because of 125 00:07:06,120 --> 00:07:07,920 Speaker 3: what I was just saying. I knew that if they 126 00:07:07,920 --> 00:07:12,280 Speaker 3: had closed my oldest credit account that would really be 127 00:07:12,360 --> 00:07:14,920 Speaker 3: a hit on my score. Turns out I had somewhere 128 00:07:14,920 --> 00:07:17,640 Speaker 3: along the lines gotten an additional Bank of America card 129 00:07:17,640 --> 00:07:20,280 Speaker 3: that they closed, But that kind of made me kind 130 00:07:20,280 --> 00:07:21,920 Speaker 3: of perk up and realize, Okay, I got to make 131 00:07:21,960 --> 00:07:24,840 Speaker 3: sure that I spend a little something on this long 132 00:07:25,120 --> 00:07:27,120 Speaker 3: running account or else it'll get closed. 133 00:07:27,320 --> 00:07:27,920 Speaker 2: Exactly. 134 00:07:28,080 --> 00:07:31,360 Speaker 1: And for a lot of us, that becomes another bureaucratic 135 00:07:31,480 --> 00:07:35,200 Speaker 1: hassle because now you have to remember another thing to 136 00:07:35,440 --> 00:07:38,840 Speaker 1: do right. People will suggest you set up a recurring 137 00:07:39,040 --> 00:07:41,680 Speaker 1: charge on a card that you never use for a 138 00:07:41,720 --> 00:07:45,080 Speaker 1: subscription like Netflix or whatever. But then you have to 139 00:07:45,240 --> 00:07:49,760 Speaker 1: also you've given yourself another monthly chore. Anyway, we're speaking 140 00:07:49,840 --> 00:07:53,280 Speaker 1: to the choir here. For Americans in the crowd, maybe 141 00:07:53,320 --> 00:07:56,800 Speaker 1: the most simplistic way to think of a credit score 142 00:07:57,080 --> 00:08:01,040 Speaker 1: is somewhere between a financial version of a report card 143 00:08:01,400 --> 00:08:05,120 Speaker 1: and a financial version of a fingerprint. 144 00:08:04,640 --> 00:08:07,920 Speaker 3: Or like it's going on your permanent record, like they 145 00:08:07,960 --> 00:08:10,120 Speaker 3: always used to say in Nickelodeon's shows. 146 00:08:10,800 --> 00:08:13,280 Speaker 1: No, the question is and permanent record is a big 147 00:08:13,280 --> 00:08:15,720 Speaker 1: part of this too. The question is why do we 148 00:08:15,840 --> 00:08:18,440 Speaker 1: have these things in the first place. We've just spent 149 00:08:18,520 --> 00:08:21,920 Speaker 1: the first part of this show complaining about the things 150 00:08:21,920 --> 00:08:24,480 Speaker 1: they're wrong with it, and we are correct, but it 151 00:08:24,640 --> 00:08:26,160 Speaker 1: is unnecessary. 152 00:08:26,200 --> 00:08:27,880 Speaker 2: Evil. You know, you're a US lender. 153 00:08:28,160 --> 00:08:31,600 Speaker 1: You have to figure out whether this stranger, this person 154 00:08:31,640 --> 00:08:34,640 Speaker 1: you don't know from a can of paint, will actually 155 00:08:34,679 --> 00:08:35,520 Speaker 1: pay you back. 156 00:08:35,640 --> 00:08:39,200 Speaker 3: Especially if they don't have any tangible assets like a 157 00:08:39,320 --> 00:08:43,400 Speaker 3: home or a property or real estate, land, whatever, things 158 00:08:43,400 --> 00:08:46,080 Speaker 3: that you can actually measure and say, Okay, this person 159 00:08:46,240 --> 00:08:50,440 Speaker 3: has this quote unquote collateral. So that forces folks like 160 00:08:50,480 --> 00:08:52,680 Speaker 3: that if they want to play, they got to pay. 161 00:08:52,760 --> 00:08:55,600 Speaker 3: They got to get into the system. 162 00:08:55,240 --> 00:08:59,040 Speaker 1: Exactly exactly and that's why you can be punished for 163 00:08:59,320 --> 00:09:01,640 Speaker 1: not participate pad in the system, for not having a 164 00:09:01,679 --> 00:09:04,760 Speaker 1: credit card. In the case of those big purchases, like 165 00:09:04,760 --> 00:09:08,000 Speaker 1: you're talking about a car or a house, many people 166 00:09:08,360 --> 00:09:11,800 Speaker 1: will not be paying for the thing all at once. 167 00:09:12,080 --> 00:09:14,640 Speaker 1: It's just not feasible you'll pay off the full cost 168 00:09:14,679 --> 00:09:17,760 Speaker 1: of the asset over some window of time. Is where 169 00:09:17,760 --> 00:09:20,560 Speaker 1: you get things like car loans and mortgages. A credit 170 00:09:20,600 --> 00:09:24,600 Speaker 1: score helps people figure out whether you are able to 171 00:09:24,679 --> 00:09:29,800 Speaker 1: make those payments consistently, and because it incorporates your past 172 00:09:30,640 --> 00:09:37,080 Speaker 1: credit shenanigans, it gives these institutions a surprisingly accurate snapshot 173 00:09:37,240 --> 00:09:40,600 Speaker 1: of your past and present, all meant to predict your 174 00:09:40,679 --> 00:09:41,839 Speaker 1: future actions. 175 00:09:41,880 --> 00:09:42,960 Speaker 2: And I didn't know this. 176 00:09:43,240 --> 00:09:47,520 Speaker 1: The idea of credit scores is surprisingly recent, which is 177 00:09:47,679 --> 00:09:51,440 Speaker 1: nuts because the concept of debt is like five thousand 178 00:09:51,520 --> 00:09:52,040 Speaker 1: years old. 179 00:09:52,440 --> 00:09:52,920 Speaker 2: That's right. 180 00:09:52,960 --> 00:09:56,000 Speaker 3: In fact, in Debt the First five thousand Years updated 181 00:09:56,040 --> 00:09:59,760 Speaker 3: and expanded by David Graeber is a fantastic resource for 182 00:09:59,800 --> 00:10:04,360 Speaker 3: this exploration. In that work, he explores how for most 183 00:10:04,360 --> 00:10:08,000 Speaker 3: of human history, what we would call credit reporting has 184 00:10:08,080 --> 00:10:14,960 Speaker 3: been deeply personal, meaning very invasive, and the kind of 185 00:10:14,960 --> 00:10:17,440 Speaker 3: thing that would be assessed on a case by case 186 00:10:17,520 --> 00:10:21,880 Speaker 3: basis by a lender who often is not going to 187 00:10:21,920 --> 00:10:25,560 Speaker 3: be offering a codified set of terms or you know, 188 00:10:26,480 --> 00:10:30,520 Speaker 3: interest rates vig let's call it, right. The history of 189 00:10:30,679 --> 00:10:34,320 Speaker 3: lending is a history of taking advantage of people, especially 190 00:10:34,360 --> 00:10:39,080 Speaker 3: before institutions, you know, for better or worse, swooped in 191 00:10:39,080 --> 00:10:40,480 Speaker 3: in more modern times to kind of. 192 00:10:40,440 --> 00:10:41,640 Speaker 2: Regulate that kind of stuff. 193 00:10:42,440 --> 00:10:46,480 Speaker 1: Yeah, travel back with those folks to the seventeen hundreds 194 00:10:46,920 --> 00:10:50,280 Speaker 1: United States for example, or what would become it. If 195 00:10:50,320 --> 00:10:54,000 Speaker 1: you're a storekeeper and you want to start a business 196 00:10:54,120 --> 00:10:56,240 Speaker 1: or you need a loan, the way you get a 197 00:10:56,360 --> 00:10:59,560 Speaker 1: loan is not by filling out a form. You go 198 00:10:59,640 --> 00:11:03,120 Speaker 1: to the most successful guy in town, hope you're friends 199 00:11:03,160 --> 00:11:08,079 Speaker 1: with them, maybe an authority like a politician or someone 200 00:11:08,640 --> 00:11:13,040 Speaker 1: in the religious structure, and you say, hey, will you 201 00:11:13,080 --> 00:11:16,000 Speaker 1: go to this banker, this merchant and tell them you know, 202 00:11:16,679 --> 00:11:19,920 Speaker 1: I know Phineas. Phineas is a good dude, and he'll 203 00:11:19,920 --> 00:11:21,960 Speaker 1: pay you for those beans. 204 00:11:22,200 --> 00:11:26,520 Speaker 2: Mm hmmm, like the cut of his jib. That's right, 205 00:11:26,679 --> 00:11:29,200 Speaker 2: you really did. It was a vibes. 206 00:11:28,960 --> 00:11:32,320 Speaker 3: Based system, ben, as you so astutely put it here. 207 00:11:33,240 --> 00:11:35,880 Speaker 3: Beginning in the eighteen twenties, however, credit reporting started to 208 00:11:35,960 --> 00:11:37,319 Speaker 3: modernize a little bit. 209 00:11:37,559 --> 00:11:39,160 Speaker 2: That's because there were just. 210 00:11:39,240 --> 00:11:43,400 Speaker 3: So many businesses and so many business transactions being made, 211 00:11:44,600 --> 00:11:48,000 Speaker 3: you know, under a kind of thriving economy that was 212 00:11:48,080 --> 00:11:50,520 Speaker 3: beginning to sort of, you know, come into its own 213 00:11:50,559 --> 00:11:53,160 Speaker 3: in a very modern way that the old ways just 214 00:11:53,200 --> 00:11:54,320 Speaker 3: didn't make sense anymore. 215 00:11:54,360 --> 00:11:55,200 Speaker 2: They just didn't hold. 216 00:11:55,440 --> 00:11:58,360 Speaker 3: So there were, like I said, there were beginning to 217 00:11:58,360 --> 00:12:01,679 Speaker 3: be new laws and regulations in place that would potentially 218 00:12:01,720 --> 00:12:05,480 Speaker 3: protect people but also potentially made loans a little bit 219 00:12:05,520 --> 00:12:09,959 Speaker 3: of a riskier proposition, because bankruptcy was a thing, meaning 220 00:12:10,000 --> 00:12:14,080 Speaker 3: that you could default on loans and not be penalized 221 00:12:14,280 --> 00:12:17,600 Speaker 3: for those it would certainly wreck you personally for a time. 222 00:12:17,679 --> 00:12:20,160 Speaker 3: Bankruptcy is no joke, but it doesn't mean that people 223 00:12:20,160 --> 00:12:23,160 Speaker 3: that have let you that capital are out in the cold. 224 00:12:23,720 --> 00:12:27,000 Speaker 1: Yeah, And bankruptcy laws, the new ones that were coming 225 00:12:27,040 --> 00:12:30,920 Speaker 1: about at this time, did, as you said, make loans riskier, 226 00:12:30,960 --> 00:12:35,640 Speaker 1: and they also provided some manners of protection or some 227 00:12:36,440 --> 00:12:40,960 Speaker 1: degree of protection for the people in businesses under those laws. 228 00:12:41,320 --> 00:12:44,040 Speaker 1: As a result of this, we see a series of 229 00:12:44,080 --> 00:12:48,320 Speaker 1: experiments in trying to standardize what we mean when we 230 00:12:48,360 --> 00:12:54,400 Speaker 1: say credit. All these early forays were limited to commercial credit, 231 00:12:54,520 --> 00:12:58,640 Speaker 1: loans to businesses to business people, but still they would 232 00:12:58,760 --> 00:13:03,800 Speaker 1: later have found implications for consumer credit, which is what 233 00:13:04,520 --> 00:13:07,400 Speaker 1: we were both complaining about at the beginning of this show. 234 00:13:08,040 --> 00:13:11,360 Speaker 1: There's a great article about this by Sean Trainer writing 235 00:13:11,440 --> 00:13:18,800 Speaker 1: for Time, and Sean introduces us to probably the most 236 00:13:18,800 --> 00:13:24,959 Speaker 1: important of those experiments in the eighteen hundreds, the Mercantile Agency, 237 00:13:25,080 --> 00:13:27,360 Speaker 1: which was created in eighteen forty. 238 00:13:27,080 --> 00:13:31,319 Speaker 3: One in the Panic of eighteen thirty seven, a depression 239 00:13:31,679 --> 00:13:36,400 Speaker 3: caused by over speculation over extension of line of credits 240 00:13:36,400 --> 00:13:40,520 Speaker 3: by merchants essentially borrowing more than they could pay back, 241 00:13:40,840 --> 00:13:44,760 Speaker 3: causing kind of a cascading effect of defaults. Tap him 242 00:13:45,120 --> 00:13:48,800 Speaker 3: Lewis tappan that you mentioned, decided he needed to systematize 243 00:13:48,960 --> 00:13:53,840 Speaker 3: that kind of vibess based form of asking for credit 244 00:13:54,200 --> 00:13:58,839 Speaker 3: in terms of, you know, debtors vouching for the borrowers 245 00:13:59,040 --> 00:14:03,760 Speaker 3: character and their assets. So he went out doing some 246 00:14:03,840 --> 00:14:08,560 Speaker 3: kind of fact finding and soliciting some information from correspondence 247 00:14:08,600 --> 00:14:13,000 Speaker 3: throughout the entire country. His agency sort of crystallized all 248 00:14:13,000 --> 00:14:16,560 Speaker 3: of this stuff into various reports that were assembled into 249 00:14:16,920 --> 00:14:21,000 Speaker 3: massive ledgers that were held in New York City. So 250 00:14:21,080 --> 00:14:24,200 Speaker 3: this was essentially the beginnings of a kind of credit 251 00:14:24,320 --> 00:14:25,920 Speaker 3: worthiness database. 252 00:14:26,360 --> 00:14:31,840 Speaker 1: Yeah, and basically these guys are spies. They're going out 253 00:14:31,880 --> 00:14:34,920 Speaker 1: to these other you know, by hook or by crook. 254 00:14:34,960 --> 00:14:37,320 Speaker 1: They're going to figure out the stuff they don't want 255 00:14:37,320 --> 00:14:39,680 Speaker 1: you to know. Right when it comes to these businesses 256 00:14:39,720 --> 00:14:44,880 Speaker 1: and their credit. The Mercantile Agency used these correspondents to, 257 00:14:45,880 --> 00:14:49,200 Speaker 1: like we're saying, get information from across the country from 258 00:14:49,480 --> 00:14:51,400 Speaker 1: lenders as well as from borrowers. 259 00:14:51,800 --> 00:14:54,280 Speaker 2: So it did become a. 260 00:14:54,240 --> 00:14:58,960 Speaker 1: Predecessor to a credit reporting agency, but it also went deep. 261 00:14:59,040 --> 00:15:01,480 Speaker 1: This is why I say sp vinage and spying. They 262 00:15:01,520 --> 00:15:05,800 Speaker 1: would figure out someone's marital status, their ethnic background, their age, 263 00:15:06,000 --> 00:15:10,240 Speaker 1: any potential social scandal. They knew about their politics and 264 00:15:10,280 --> 00:15:14,840 Speaker 1: their sex lives. And they were super duper biased. The 265 00:15:14,920 --> 00:15:19,760 Speaker 1: reporters were primarily white and male, and they had no 266 00:15:20,360 --> 00:15:24,280 Speaker 1: they did not hesitate to include their prejudices in their 267 00:15:24,920 --> 00:15:30,200 Speaker 1: in their conversations, things zero scruples. They thought that they 268 00:15:30,200 --> 00:15:32,760 Speaker 1: were we can only imagine they thought that they were 269 00:15:32,800 --> 00:15:36,920 Speaker 1: doing ethical reporting, but it was based on their own, 270 00:15:37,240 --> 00:15:40,120 Speaker 1: you know, their own social perspective. There was one credit 271 00:15:40,160 --> 00:15:46,080 Speaker 1: reporter from Buffalo, New York who noted quote prudence and 272 00:15:46,200 --> 00:15:49,080 Speaker 1: large transactions with all Jews. 273 00:15:48,960 --> 00:15:54,760 Speaker 2: Should be used. No, yes, he did not say that. 274 00:15:54,920 --> 00:15:59,680 Speaker 1: Yeah, so you know, the argument being, hey, this business 275 00:15:59,760 --> 00:16:02,520 Speaker 1: looks good on paper, but I did some digging and 276 00:16:02,720 --> 00:16:04,680 Speaker 1: I remembered I'm anti Semitic. 277 00:16:06,080 --> 00:16:08,680 Speaker 3: That's yeah, just so jacked up too, because there's also 278 00:16:08,800 --> 00:16:12,200 Speaker 3: the anti Semitic perspective that Jews like are really good 279 00:16:12,200 --> 00:16:15,160 Speaker 3: with money, you know, like it's it's I just like, 280 00:16:15,560 --> 00:16:18,760 Speaker 3: it's weird how you can see racist people wanting to 281 00:16:18,800 --> 00:16:19,520 Speaker 3: have it both ways. 282 00:16:19,600 --> 00:16:22,280 Speaker 1: Yeah, you know what I mean, and not even mentioning 283 00:16:22,520 --> 00:16:27,080 Speaker 1: you know, the the racism against African Americans, right. 284 00:16:27,040 --> 00:16:29,960 Speaker 2: Well we should mention it a little bit. Yeah, yeah, yeah. 285 00:16:30,440 --> 00:16:34,000 Speaker 3: Another reporter in post Civil War Georgia described a liquor 286 00:16:34,000 --> 00:16:35,200 Speaker 3: store owner a G. 287 00:16:35,440 --> 00:16:35,800 Speaker 2: Marx. 288 00:16:36,520 --> 00:16:41,440 Speaker 3: His business described it as being a low Negro shop. 289 00:16:42,400 --> 00:16:46,600 Speaker 1: Gross gross, And this means that there's a much higher 290 00:16:46,680 --> 00:16:51,440 Speaker 1: likelihood of loans being denied or I would say equally 291 00:16:51,480 --> 00:16:58,000 Speaker 1: as as probable having very predatory loans extended and saying, hey, 292 00:16:58,040 --> 00:17:01,440 Speaker 1: we're the only folks who will play ball with you anyway. Yeah, 293 00:17:01,480 --> 00:17:06,800 Speaker 1: this discrimination had profound consequences. Again, first, it reinforced existing 294 00:17:06,880 --> 00:17:10,840 Speaker 1: social inequalities, just like the later practice that we're all 295 00:17:10,840 --> 00:17:14,600 Speaker 1: familiar with redlining, which is where in the world of 296 00:17:14,640 --> 00:17:21,159 Speaker 1: real estate it's a way to systemize racial discrimination. But 297 00:17:21,280 --> 00:17:25,320 Speaker 1: this was all still no, this was all still a 298 00:17:25,359 --> 00:17:29,680 Speaker 1: collection of rumors. This was a whisper factory. People were 299 00:17:29,800 --> 00:17:33,440 Speaker 1: just turn they were getting paid to turn in reports 300 00:17:33,440 --> 00:17:38,120 Speaker 1: that were like, uh, this guy is supposed to have money. 301 00:17:38,480 --> 00:17:41,879 Speaker 1: People at the local you know, at the local cracker 302 00:17:41,920 --> 00:17:44,080 Speaker 1: barrel say he has money. Or people at the local 303 00:17:44,119 --> 00:17:49,720 Speaker 1: saloon say he is a real cad, but he's good 304 00:17:49,720 --> 00:17:52,000 Speaker 1: at business and it'll pay us back. That's not how 305 00:17:52,080 --> 00:17:53,320 Speaker 1: credit reporting should work. 306 00:17:53,920 --> 00:17:56,240 Speaker 3: No, And like you know, I mean, there's part of 307 00:17:56,240 --> 00:17:59,320 Speaker 3: me that was like, okay, good, good job to try 308 00:17:59,359 --> 00:18:04,680 Speaker 3: to pull together some kind of less vibes based, sort 309 00:18:04,680 --> 00:18:07,040 Speaker 3: of systematized. 310 00:18:06,600 --> 00:18:07,800 Speaker 2: Method for credit reporting. 311 00:18:07,840 --> 00:18:10,679 Speaker 3: But really all this did was give the power to 312 00:18:10,800 --> 00:18:14,840 Speaker 3: a select few on a much larger scale. Well, at 313 00:18:14,920 --> 00:18:18,040 Speaker 3: least before you could like talk to your buddy and 314 00:18:18,080 --> 00:18:21,199 Speaker 3: get a loan, you know, whatever your ethnic background was, 315 00:18:21,240 --> 00:18:24,000 Speaker 3: if your buddy was cool with you, he'd say good 316 00:18:24,000 --> 00:18:26,239 Speaker 3: things and then you'd get the lung. But now you've 317 00:18:26,280 --> 00:18:28,639 Speaker 3: got people that you can't even see face to face 318 00:18:28,800 --> 00:18:32,480 Speaker 3: to make your case, digging and snooping into your business 319 00:18:32,520 --> 00:18:37,440 Speaker 3: and adding some pretty gnarly hyperbolic, you know, little twists 320 00:18:37,560 --> 00:18:40,320 Speaker 3: to these reports, twists of the knife that are not 321 00:18:40,359 --> 00:18:42,040 Speaker 3: really necessarily based in reality. 322 00:18:42,600 --> 00:18:46,200 Speaker 1: Yeah, exactly, they're they're kind of pushing their own thing 323 00:18:46,560 --> 00:18:52,160 Speaker 1: into the conversation, and people are realizing the problem here. 324 00:18:52,200 --> 00:18:56,520 Speaker 1: These are not government agencies. The Mercantile Agency and its rival, 325 00:18:56,680 --> 00:18:59,920 Speaker 1: the Bradstreet Company, which is kind of Pepsi to the coke. Here, 326 00:19:00,680 --> 00:19:05,560 Speaker 1: their private entities, and people pay for their services. They 327 00:19:05,680 --> 00:19:09,480 Speaker 1: are increasingly irritated with this because it's messing. 328 00:19:09,200 --> 00:19:09,800 Speaker 2: With their money. 329 00:19:09,880 --> 00:19:14,080 Speaker 1: So they say, we want a more simplified, reliable, less 330 00:19:14,280 --> 00:19:20,920 Speaker 1: subjective evaluation method. And this creates what our buddy Sean 331 00:19:20,960 --> 00:19:32,119 Speaker 1: Trainor calls a pseudo scientific sleight of hand. They said, okay, 332 00:19:32,119 --> 00:19:35,920 Speaker 1: we'll take these vibes you're elected and make them into masks. Yeah, 333 00:19:36,119 --> 00:19:40,760 Speaker 1: well you've created quantify them. We'll use alchemy that sounds 334 00:19:40,840 --> 00:19:43,800 Speaker 1: legit until you learn about it. We'll use that to 335 00:19:43,840 --> 00:19:48,080 Speaker 1: make it sound like we have mathematically determined something. 336 00:19:48,800 --> 00:19:50,360 Speaker 2: It lends it more credence. 337 00:19:50,400 --> 00:19:53,040 Speaker 3: When it's like this number that you can understand or 338 00:19:53,080 --> 00:19:55,080 Speaker 3: you can see on paper, say oh okay, that number 339 00:19:55,200 --> 00:19:58,280 Speaker 3: is high. That might I'm a person must be very reliable, 340 00:19:58,520 --> 00:20:02,520 Speaker 3: but you still it it hides all of the factors 341 00:20:02,520 --> 00:20:06,479 Speaker 3: that actually go into what that number is. By design, 342 00:20:06,640 --> 00:20:10,280 Speaker 3: pioneered by the Bradstreet Company in eighteen fifty seven, commercial 343 00:20:10,280 --> 00:20:14,080 Speaker 3: credit rating would assume a much more lasting form. In 344 00:20:14,119 --> 00:20:19,879 Speaker 3: eighteen sixty four, under the Mercantile Agency renamed RG. Dunn 345 00:20:19,960 --> 00:20:23,480 Speaker 3: and Company, on the very eve of the Civil War, 346 00:20:23,920 --> 00:20:29,600 Speaker 3: it finalized this numeric system, alphanumeric system that would continue 347 00:20:29,920 --> 00:20:33,480 Speaker 3: in use well into the twenty first century. The companies 348 00:20:33,560 --> 00:20:39,800 Speaker 3: later merged like they do, becoming done and Bradstreet a 349 00:20:38,520 --> 00:20:40,560 Speaker 3: real monopoly. 350 00:20:41,080 --> 00:20:42,080 Speaker 2: Yeah, very much so. 351 00:20:42,640 --> 00:20:46,640 Speaker 1: And as as the musician said, you ain't seen nothing yet, 352 00:20:46,800 --> 00:20:51,640 Speaker 1: but bah bah baby. So this is this is strange 353 00:20:51,680 --> 00:20:58,040 Speaker 1: because credit scoring factored in a lot of other large 354 00:20:58,240 --> 00:21:05,320 Speaker 1: scale quantification operas of the time, population management, statistical analysis, espionage. 355 00:21:05,560 --> 00:21:07,640 Speaker 1: But it was still it was novel in its own 356 00:21:07,720 --> 00:21:12,800 Speaker 1: right because it had what we could call financial identity, 357 00:21:13,119 --> 00:21:18,680 Speaker 1: Like you said, a number or an alphanumeric system that says, 358 00:21:18,760 --> 00:21:21,560 Speaker 1: we're going to summarize everything you need to know about 359 00:21:21,560 --> 00:21:24,520 Speaker 1: this person. We'll give you the elevator pitch about this person, 360 00:21:25,160 --> 00:21:30,320 Speaker 1: and that number that rating will change if they suffer 361 00:21:30,560 --> 00:21:34,160 Speaker 1: what we will call a lapse in fortune or discipline. 362 00:21:34,880 --> 00:21:38,440 Speaker 1: And the people who loved it had these glowing terms. 363 00:21:38,760 --> 00:21:42,639 Speaker 1: One guy said, the Mercantile Agency might be well termed 364 00:21:43,359 --> 00:21:46,399 Speaker 1: Bureau for the Promotion of honesty. 365 00:21:46,920 --> 00:21:51,960 Speaker 2: Cool. Sure, yeah, full story seems legit now. So by 366 00:21:51,960 --> 00:21:53,360 Speaker 2: the end of the Civil. 367 00:21:53,040 --> 00:21:57,439 Speaker 3: War there were these kind of three steadfast pillars of 368 00:21:57,520 --> 00:21:58,840 Speaker 3: modern credit reporting. 369 00:21:59,359 --> 00:22:02,440 Speaker 2: Private sector mass surveillance that made credit. 370 00:22:02,200 --> 00:22:10,200 Speaker 3: Reports possible, a deep, deep bureaucracy involving information sharing, making 371 00:22:11,119 --> 00:22:17,440 Speaker 3: the rating system widely available, and also accept it by 372 00:22:18,000 --> 00:22:22,120 Speaker 3: kind of polite society and by you know, big industries, 373 00:22:22,400 --> 00:22:27,639 Speaker 3: and also made them actionable and acknowledged widely by you know, 374 00:22:28,440 --> 00:22:33,040 Speaker 3: large corporations, entities that could lend money on a large scale. 375 00:22:33,400 --> 00:22:33,840 Speaker 2: All of that. 376 00:22:33,960 --> 00:22:36,040 Speaker 3: So, all of this was still for the purposes of 377 00:22:36,119 --> 00:22:39,879 Speaker 3: business and not yet for individuals, right ben This would 378 00:22:40,480 --> 00:22:42,800 Speaker 3: to get to that place would take quite a bit longer. 379 00:22:43,280 --> 00:22:43,600 Speaker 2: Yeah. 380 00:22:43,920 --> 00:22:49,360 Speaker 1: Yeah. And the lessons learned in commercial credit evaluation did 381 00:22:49,440 --> 00:22:55,119 Speaker 1: deeply inform what we call consumer credit today, and the 382 00:22:55,240 --> 00:23:01,080 Speaker 1: stories are weirdly similar. So, just like with a consumer debt, 383 00:23:01,280 --> 00:23:05,240 Speaker 1: consumer credit reporting wasn't really a needed thing in early 384 00:23:05,320 --> 00:23:12,879 Speaker 1: America because there wasn't a density of business propositions. There 385 00:23:12,880 --> 00:23:16,480 Speaker 1: were fewer people, there were fewer jobs. It was easier 386 00:23:17,040 --> 00:23:20,040 Speaker 1: for folks to know each other personally. But then by 387 00:23:20,080 --> 00:23:23,240 Speaker 1: the second half of the eighteen hundreds, the success of 388 00:23:23,280 --> 00:23:26,240 Speaker 1: the labor movement means there are a lot of people 389 00:23:26,320 --> 00:23:29,680 Speaker 1: who are working less and making more and they deserved 390 00:23:29,680 --> 00:23:34,080 Speaker 1: it for the record, and this meant that retailers, including 391 00:23:34,119 --> 00:23:38,000 Speaker 1: those fancy pants guys at the department stores and in 392 00:23:38,040 --> 00:23:40,679 Speaker 1: the auto industry, they wanted to get some of that 393 00:23:40,880 --> 00:23:44,560 Speaker 1: hard earned middle class money. So they made their own 394 00:23:44,680 --> 00:23:49,359 Speaker 1: credit lines. Your financial reputation, you was a person, became 395 00:23:49,440 --> 00:23:51,919 Speaker 1: another product from the private market. 396 00:23:52,240 --> 00:23:55,520 Speaker 3: Yeah, and we delve deep into this in our episode 397 00:23:55,560 --> 00:23:57,639 Speaker 3: on credit cards, because this really is the kind of 398 00:23:57,680 --> 00:24:01,080 Speaker 3: advent of the modern credit card. They even like had 399 00:24:01,160 --> 00:24:04,440 Speaker 3: these sort of like proprietary printed you know whatever. 400 00:24:04,480 --> 00:24:06,320 Speaker 2: Some of them I think were even like pieces of 401 00:24:06,359 --> 00:24:07,120 Speaker 2: metal or something. 402 00:24:07,200 --> 00:24:10,359 Speaker 1: Yeah, that guy who who just wanted to not have 403 00:24:10,440 --> 00:24:12,040 Speaker 1: to immediately pay cash. 404 00:24:11,800 --> 00:24:14,800 Speaker 2: For dinner in New York, that's how it started. Yeah, 405 00:24:14,840 --> 00:24:16,080 Speaker 2: it was a real flex that's true. 406 00:24:16,080 --> 00:24:18,719 Speaker 3: So the system was, of course, as we've you know, 407 00:24:19,080 --> 00:24:21,639 Speaker 3: already kind of detailed up to this point didn't really change, 408 00:24:21,760 --> 00:24:24,919 Speaker 3: rife with misinformation and absolutely. 409 00:24:25,080 --> 00:24:27,560 Speaker 2: Ripe for corruption and abuse. 410 00:24:28,240 --> 00:24:31,720 Speaker 3: It definitely paved the way for the coming great depression, 411 00:24:31,760 --> 00:24:33,480 Speaker 3: which is just not good. 412 00:24:33,560 --> 00:24:34,960 Speaker 2: Wasn't great at all. It was pretty awful. 413 00:24:36,240 --> 00:24:40,600 Speaker 3: But it also did help create that middle class that 414 00:24:41,080 --> 00:24:44,879 Speaker 3: pursuit right that we always talk about of happiness, the 415 00:24:44,960 --> 00:24:48,560 Speaker 3: idea that you too can live the catalog dream of 416 00:24:48,560 --> 00:24:49,359 Speaker 3: American life. 417 00:24:49,520 --> 00:24:52,120 Speaker 2: Give it a go. You could pursue it, my guy. 418 00:24:52,800 --> 00:24:58,639 Speaker 1: So the men and women who were evaluating consumer credit 419 00:24:58,760 --> 00:25:05,240 Speaker 1: at these various different retail outfits, they were still decentralized. 420 00:25:05,280 --> 00:25:09,320 Speaker 1: There was no single dominant firm in charge of this. Instead, 421 00:25:09,640 --> 00:25:15,119 Speaker 1: these individuals were employed directly by retailers as credit managers, 422 00:25:15,560 --> 00:25:19,200 Speaker 1: but they were in the know. These were all smart cookies. 423 00:25:19,440 --> 00:25:22,880 Speaker 1: They picked up the techniques pioneered by those earlier firms, 424 00:25:22,920 --> 00:25:27,119 Speaker 1: the done Men Bradstreet types. In nineteen twelve, the credit 425 00:25:27,200 --> 00:25:31,840 Speaker 1: managers get together and form a national trade association and 426 00:25:31,920 --> 00:25:35,919 Speaker 1: they start comparing notes. How do you best collect, share 427 00:25:36,160 --> 00:25:41,240 Speaker 1: and codify or uniform eyes information on the people who 428 00:25:41,240 --> 00:25:43,359 Speaker 1: go to your car dealership or the people who go 429 00:25:43,440 --> 00:25:46,879 Speaker 1: to seers. It's like the early days of sodopop. There 430 00:25:46,920 --> 00:25:49,359 Speaker 1: are a lot of things there for a second, and 431 00:25:49,400 --> 00:25:50,160 Speaker 1: then a lot of. 432 00:25:50,119 --> 00:25:52,800 Speaker 2: Them fizzle out. 433 00:25:54,880 --> 00:25:59,440 Speaker 1: Oh right, jeez, But we got to give a shout out, 434 00:25:59,720 --> 00:26:03,040 Speaker 1: to be a shout out to our fair metropolis. 435 00:26:03,320 --> 00:26:03,680 Speaker 2: Uh nul. 436 00:26:03,800 --> 00:26:08,080 Speaker 1: One of the most successful firms in this era was 437 00:26:08,200 --> 00:26:10,879 Speaker 1: Atlanta's Retail Credit Company. 438 00:26:11,320 --> 00:26:11,560 Speaker 2: Yeah. 439 00:26:11,720 --> 00:26:17,000 Speaker 3: The Atlanta Retail Credit Company absolutely left a mark on 440 00:26:17,160 --> 00:26:19,560 Speaker 3: the idea of credit reporting. It was found in eighteen 441 00:26:19,640 --> 00:26:24,520 Speaker 3: ninety nine. It developed files on millions of Americans over 442 00:26:25,160 --> 00:26:28,840 Speaker 3: the span of sixty years, and this information included not 443 00:26:28,960 --> 00:26:35,040 Speaker 3: just credit data assets character again very vague, but also 444 00:26:35,240 --> 00:26:40,960 Speaker 3: information on individuals, social, political and yes, sexual lives. 445 00:26:41,119 --> 00:26:45,000 Speaker 2: Dangerous. He's good with money, but he's a known fraudarist 446 00:26:45,400 --> 00:26:48,080 Speaker 2: and a communist. Yeah. 447 00:26:48,840 --> 00:26:53,200 Speaker 1: RCC was kind of like comcasts. They were not popular 448 00:26:53,200 --> 00:26:56,719 Speaker 1: with the public, but they were very big in their field, 449 00:26:57,160 --> 00:27:01,119 Speaker 1: and people were increasingly skeptical of their their process. 450 00:27:01,960 --> 00:27:03,680 Speaker 2: For a long time. 451 00:27:03,840 --> 00:27:07,680 Speaker 1: For the earliest decades, they were collecting this information, storing 452 00:27:07,720 --> 00:27:12,160 Speaker 1: it on paper in file cabinets, filing cabinets. But in 453 00:27:12,200 --> 00:27:16,280 Speaker 1: the nineteen sixties the firm went public with their plan 454 00:27:16,440 --> 00:27:20,200 Speaker 1: to computerize their records, making them much easier to access. 455 00:27:20,400 --> 00:27:22,960 Speaker 3: And ben I gotta give you props for this quote 456 00:27:22,960 --> 00:27:25,400 Speaker 3: you found in the New York Times from Alan Weston, 457 00:27:25,400 --> 00:27:31,639 Speaker 3: who's privacy advocate from nineteen sixty eight. I feel like 458 00:27:31,680 --> 00:27:36,440 Speaker 3: he's predicting the future here, almost inevitably. He said, transferring 459 00:27:36,480 --> 00:27:39,119 Speaker 3: information from a manual file onto a computer triggers a 460 00:27:39,160 --> 00:27:42,200 Speaker 3: threat to civil liberties, to privacy, to a man's very 461 00:27:42,280 --> 00:27:44,919 Speaker 3: humanity because access is so simple. 462 00:27:46,359 --> 00:27:51,359 Speaker 1: Yeah, he feared that digitization would make it impossible for 463 00:27:51,480 --> 00:27:55,480 Speaker 1: the average person to move beyond past missteps or mistakes, 464 00:27:55,800 --> 00:28:00,159 Speaker 1: no matter when they occurred, and dantada, he was not 465 00:28:00,280 --> 00:28:00,800 Speaker 1: far off. 466 00:28:01,359 --> 00:28:04,880 Speaker 3: Now, he also wasn't the first to make this observation 467 00:28:05,600 --> 00:28:07,480 Speaker 3: and to make this argument as far back as in 468 00:28:07,520 --> 00:28:11,920 Speaker 3: nineteen thirty six, Time magazine explored one particular woman's move 469 00:28:11,920 --> 00:28:15,960 Speaker 3: from Chicago to Los Angeles and how quickly her debts 470 00:28:16,280 --> 00:28:19,640 Speaker 3: were and dark financial pass well and personal pass were 471 00:28:19,680 --> 00:28:26,280 Speaker 3: discovered by credit reporters. In this expose, it showed the 472 00:28:26,920 --> 00:28:32,120 Speaker 3: frightening power of this surveillance state type operation and how 473 00:28:32,160 --> 00:28:38,080 Speaker 3: incredibly dangerous credit reporting agencies could be for individuals. 474 00:28:38,600 --> 00:28:39,280 Speaker 2: Yeah. 475 00:28:39,520 --> 00:28:46,080 Speaker 1: Yeah, and you know, the privacy violations alone are deeply concerning, 476 00:28:46,160 --> 00:28:50,760 Speaker 1: but also consider this kind of information if computerized, and 477 00:28:50,840 --> 00:28:52,680 Speaker 1: if there was a way to make it widely available, 478 00:28:53,120 --> 00:28:55,880 Speaker 1: it could be used to aid and a bet some 479 00:28:56,080 --> 00:28:59,360 Speaker 1: terrible things like a lot of people who skip town 480 00:29:00,240 --> 00:29:03,680 Speaker 1: trying to escape domestic violence situations. So what if your 481 00:29:03,800 --> 00:29:08,040 Speaker 1: abuser can figure out where you have gone and then 482 00:29:08,120 --> 00:29:11,520 Speaker 1: find you. So this is I mean, that's just one case, 483 00:29:11,600 --> 00:29:14,560 Speaker 1: but there are a lot of concerns. This computerization also 484 00:29:15,320 --> 00:29:21,480 Speaker 1: galvanized the moves toward mergers and acquisition and monopolization. In 485 00:29:21,560 --> 00:29:25,760 Speaker 1: the nineteen sixties, there were more than two thousand credit 486 00:29:25,800 --> 00:29:30,080 Speaker 1: bureaus across the United States, and by let's see the 487 00:29:30,200 --> 00:29:35,080 Speaker 1: nineteen eighties, that number had shrunk to five, and then 488 00:29:35,120 --> 00:29:38,600 Speaker 1: it shrunk to the three major credit bureaus we have today. 489 00:29:38,760 --> 00:29:41,239 Speaker 3: I had no idea that it was that type of 490 00:29:41,280 --> 00:29:45,040 Speaker 3: a timeline. Two thousand credit bureau that just seems so 491 00:29:45,400 --> 00:29:50,400 Speaker 3: untenable because they're all operating with different metrics, you know, Like, 492 00:29:50,480 --> 00:29:52,600 Speaker 3: I mean, how could you make heads or tails of 493 00:29:52,640 --> 00:29:55,800 Speaker 3: that with that many credit reporting bureaus, even three, like 494 00:29:55,840 --> 00:29:57,560 Speaker 3: I was saying at the top of the show, feels 495 00:29:57,560 --> 00:30:01,360 Speaker 3: a little muddled. They don't agree, they don't agree, But 496 00:30:01,600 --> 00:30:03,880 Speaker 3: again it is sorry, I can't can't stay enough, if 497 00:30:03,920 --> 00:30:06,280 Speaker 3: you go to a credit Karma, you can see your 498 00:30:06,520 --> 00:30:09,920 Speaker 3: two of the major in the top credit reporting bureaus. 499 00:30:09,920 --> 00:30:11,560 Speaker 3: You can see your score on both of those that 500 00:30:11,640 --> 00:30:15,480 Speaker 3: kind of compare them and see how various factors play 501 00:30:15,560 --> 00:30:17,720 Speaker 3: into what that score is for the different bureaus. 502 00:30:18,000 --> 00:30:20,280 Speaker 1: Yeah, and there's a guy who gets mentioned a lot 503 00:30:20,360 --> 00:30:24,240 Speaker 1: in these explorations, Josh Louer, who is the author of 504 00:30:24,280 --> 00:30:27,600 Speaker 1: a book called credit Worthy History of Consumer Surveillance and 505 00:30:27,680 --> 00:30:32,520 Speaker 1: Financial Identity in America, and he helps shed some light 506 00:30:32,560 --> 00:30:37,840 Speaker 1: on how this monopolization occurs. He says, look, before the sixties, 507 00:30:37,960 --> 00:30:40,480 Speaker 1: all the files were in filing cabinets, they were on 508 00:30:40,560 --> 00:30:44,160 Speaker 1: paper and cards. There were these bureaus that were doing well. 509 00:30:44,200 --> 00:30:46,360 Speaker 1: They had a lot of money. What they would do 510 00:30:46,560 --> 00:30:50,000 Speaker 1: is come and find a local or regional credit bureau. 511 00:30:50,320 --> 00:30:53,320 Speaker 1: They would buy up all their information. They'd acquire that 512 00:30:53,440 --> 00:30:56,560 Speaker 1: smaller interest than they would computerize it and add it 513 00:30:56,600 --> 00:30:58,480 Speaker 1: to their dragon's hoard of data. 514 00:30:58,920 --> 00:31:02,120 Speaker 2: Is any of this sound familiar, y'all, like banking? No? 515 00:31:02,120 --> 00:31:05,160 Speaker 3: No, just the idea of like your personal data being 516 00:31:05,240 --> 00:31:09,680 Speaker 3: bought and sold and traded like Pokemon cards, Like it's 517 00:31:10,240 --> 00:31:10,760 Speaker 3: not new. 518 00:31:12,000 --> 00:31:14,440 Speaker 4: When we were going through like that, earlier or what 519 00:31:14,480 --> 00:31:16,560 Speaker 4: they're talking about, like you know, your sex life, ethnicity. 520 00:31:16,600 --> 00:31:19,840 Speaker 4: I'm like, I'm listening to this. Sounds like Facebook to me, 521 00:31:20,440 --> 00:31:22,240 Speaker 4: information era like level stuff. 522 00:31:22,280 --> 00:31:25,120 Speaker 3: Just definitely exactly, that's the term I was thinking of 523 00:31:25,200 --> 00:31:27,680 Speaker 3: max data mining, but like you know, and then the 524 00:31:27,680 --> 00:31:31,400 Speaker 3: whole idea of changing the credit reporting to something that's 525 00:31:31,560 --> 00:31:37,560 Speaker 3: more fact based and less you know, perhaps personal information based. 526 00:31:38,760 --> 00:31:41,120 Speaker 2: Sure that's how that industry evolved. 527 00:31:41,120 --> 00:31:43,400 Speaker 3: Perhaps, but then you still have you know, all of 528 00:31:43,440 --> 00:31:46,760 Speaker 3: the social media stuff and all that information floating out there, 529 00:31:46,880 --> 00:31:49,480 Speaker 3: and it can't not have an impact I would imagine, 530 00:31:49,520 --> 00:31:52,160 Speaker 3: you know, on things like insurability for example. You know, 531 00:31:52,240 --> 00:31:55,160 Speaker 3: if you have images out there of yourself doing certain things, 532 00:31:56,160 --> 00:31:58,640 Speaker 3: your insurance company could catch way to that and drop you, 533 00:31:58,840 --> 00:32:00,000 Speaker 3: drop you from your policy. 534 00:32:00,160 --> 00:32:03,200 Speaker 2: I mean, you know, this stuff that has real world implications. 535 00:32:02,800 --> 00:32:06,760 Speaker 1: And that's important to know, especially for all of our younger, 536 00:32:06,880 --> 00:32:10,400 Speaker 1: ridiculous historians in the crowd. The streets are in a 537 00:32:10,520 --> 00:32:16,320 Speaker 1: very real way watching and the erosion of privacy is 538 00:32:16,360 --> 00:32:18,960 Speaker 1: a real thing. It is being used against you, so 539 00:32:19,160 --> 00:32:23,320 Speaker 1: be very careful what you post. The dopamine rush from 540 00:32:23,480 --> 00:32:25,000 Speaker 1: likes and clicks is not worth it. 541 00:32:25,640 --> 00:32:28,040 Speaker 2: So this is that's had a little honest as you 542 00:32:28,280 --> 00:32:32,600 Speaker 2: but accorate. Yeah, but the individual results may vary. You know. 543 00:32:32,880 --> 00:32:35,600 Speaker 3: I've kind of chosen to be a one platform kind 544 00:32:35,600 --> 00:32:38,600 Speaker 3: of guy. I'm Instagram all the way, and I've curated 545 00:32:38,600 --> 00:32:41,320 Speaker 3: my algorithm to feed me lots of nice food pics 546 00:32:41,360 --> 00:32:45,560 Speaker 3: and art stuff and travel things, and I don't think 547 00:32:45,600 --> 00:32:48,280 Speaker 3: I put too too much personal information out there. 548 00:32:48,320 --> 00:32:50,360 Speaker 2: I'm pretty comfortable with the balance that I've struck. 549 00:32:50,640 --> 00:32:54,600 Speaker 1: I'm on Instagram as well. I'm on several of those platforms, 550 00:32:54,640 --> 00:32:58,480 Speaker 1: just primarily to interact with authors I like, and then. 551 00:32:58,600 --> 00:32:59,960 Speaker 2: Being public facing. Fok. 552 00:33:00,680 --> 00:33:03,280 Speaker 1: I gotta say, one of the best things you can do, folks, 553 00:33:03,320 --> 00:33:05,720 Speaker 1: just very quick opsect not to get too stuff. They 554 00:33:05,760 --> 00:33:09,720 Speaker 1: don't want you to know if you're traveling, don't post 555 00:33:09,760 --> 00:33:13,760 Speaker 1: specifics about you traveling until after you're back home safe. 556 00:33:14,600 --> 00:33:18,160 Speaker 1: Just don't do it. That's how you get got anyway. 557 00:33:18,440 --> 00:33:24,400 Speaker 1: This outcry of computerization of an information state emerging in 558 00:33:24,440 --> 00:33:29,040 Speaker 1: the private industry. This resulted in some legal action, thankfully, 559 00:33:29,640 --> 00:33:33,280 Speaker 1: most notably the Fair Credit Reporting Act of nineteen seventy, 560 00:33:33,720 --> 00:33:37,000 Speaker 1: which said, look, if you're a bureau you can't keep 561 00:33:37,040 --> 00:33:40,280 Speaker 1: black boxing stuff. Open your files to the public. You 562 00:33:40,440 --> 00:33:45,880 Speaker 1: also have to expunge data on race, sexuality or disability, 563 00:33:46,120 --> 00:33:48,880 Speaker 1: and after a certain window of time you have to 564 00:33:48,920 --> 00:33:51,600 Speaker 1: delete some negative information. That's where we get the old 565 00:33:51,600 --> 00:33:53,920 Speaker 1: trope of like this stays on your report for you know, 566 00:33:54,080 --> 00:33:56,560 Speaker 1: seven years or wherever. Clark Howard says. 567 00:33:56,520 --> 00:33:59,200 Speaker 2: Those hard inquiries. I forget the exact now. 568 00:33:59,320 --> 00:34:01,920 Speaker 3: I think is a variable, but I think it's up 569 00:34:01,960 --> 00:34:04,800 Speaker 3: to two years, and they can drop off quicker than that, 570 00:34:04,880 --> 00:34:07,880 Speaker 3: depending on other factors, some of which are not as 571 00:34:07,960 --> 00:34:10,719 Speaker 3: obvious as maybe one would like. But I think it's 572 00:34:10,800 --> 00:34:13,520 Speaker 3: wild that we didn't have a Fair Credit Reporting Act 573 00:34:13,640 --> 00:34:17,319 Speaker 3: until nineteen seventy. Up to that point, as you could 574 00:34:17,320 --> 00:34:20,319 Speaker 3: probably imagine, with those two thousand agencies, it was an 575 00:34:20,360 --> 00:34:24,560 Speaker 3: absolute free for all, you know, like truly a period 576 00:34:24,920 --> 00:34:26,000 Speaker 3: of zero. 577 00:34:25,800 --> 00:34:27,480 Speaker 2: Oversight, wild West kind of stuff. 578 00:34:27,960 --> 00:34:30,040 Speaker 3: So this was a really big deal, the idea that 579 00:34:30,080 --> 00:34:33,440 Speaker 3: you could no longer use things like race, sexuality, disability 580 00:34:34,000 --> 00:34:37,000 Speaker 3: as a metric for someone's credit score, which I think 581 00:34:37,080 --> 00:34:37,799 Speaker 3: is a huge deal. 582 00:34:37,960 --> 00:34:50,279 Speaker 2: But it certainly did not end credit reporting. No, no, no, 583 00:34:50,360 --> 00:34:51,720 Speaker 2: it put gas on the fire. 584 00:34:52,680 --> 00:34:57,520 Speaker 1: Our buddies in Atlanta RCC, they got the risk slapped 585 00:34:57,560 --> 00:35:01,399 Speaker 1: in congressional hearings but as result, they didn't go under. 586 00:35:01,520 --> 00:35:05,000 Speaker 1: They changed their name to Equifax in nineteen seventy five, 587 00:35:05,120 --> 00:35:09,080 Speaker 1: and they kept going with the with the worship of 588 00:35:09,120 --> 00:35:13,200 Speaker 1: the algorithm and computerization. I do want to know another 589 00:35:13,280 --> 00:35:18,120 Speaker 1: thing that shows us how close history is. It wasn't 590 00:35:18,239 --> 00:35:22,080 Speaker 1: until the Equal Credit Opportunity Act of nineteen seventy four 591 00:35:22,719 --> 00:35:28,080 Speaker 1: that unmarried women could get credit cards largely banks can 592 00:35:28,160 --> 00:35:32,480 Speaker 1: refuse them. So good things came about with these with 593 00:35:32,560 --> 00:35:37,080 Speaker 1: this outcry and with this legal action, but the pace 594 00:35:37,280 --> 00:35:41,400 Speaker 1: of hoovering up data only accelerated. This is where you know, 595 00:35:41,520 --> 00:35:44,719 Speaker 1: later we get Experience and TransUnion. Those are the big 596 00:35:44,760 --> 00:35:50,120 Speaker 1: three we talk about. But they're still doing better. They're 597 00:35:50,200 --> 00:35:50,600 Speaker 1: just not. 598 00:35:52,400 --> 00:35:52,920 Speaker 2: Perfect. 599 00:35:53,440 --> 00:35:57,640 Speaker 1: They're deeply imperfect. Like you said, they don't always agree 600 00:35:57,680 --> 00:35:59,640 Speaker 1: with each other. It can be tough to interpret and 601 00:35:59,680 --> 00:36:01,239 Speaker 1: compare their reports too. 602 00:36:01,800 --> 00:36:03,120 Speaker 2: That is very true. 603 00:36:03,160 --> 00:36:04,520 Speaker 3: And I also do want to play on I know 604 00:36:04,560 --> 00:36:07,799 Speaker 3: I've been beating the drum for credit karma. I did 605 00:36:07,880 --> 00:36:10,279 Speaker 3: once and I'll never do this again. I think for 606 00:36:10,400 --> 00:36:12,640 Speaker 3: my next car purchase, I'm gonna do like Carvon or 607 00:36:12,719 --> 00:36:16,200 Speaker 3: one of those zero dealership. I just don't want to 608 00:36:16,200 --> 00:36:18,200 Speaker 3: do a deal. I don't like dealerships. I don't like 609 00:36:18,239 --> 00:36:19,920 Speaker 3: the way they treat you. I don't like the way 610 00:36:19,920 --> 00:36:22,200 Speaker 3: they bait and switch you. Sorry, if there's anyone out 611 00:36:22,239 --> 00:36:25,120 Speaker 3: there that's a car dealer, and you correct me if 612 00:36:25,160 --> 00:36:27,839 Speaker 3: I'm wrong, Maybe there's good ones, but my experience has 613 00:36:27,880 --> 00:36:33,520 Speaker 3: been not good, very high pressure, very like. I literally 614 00:36:33,600 --> 00:36:35,719 Speaker 3: had someone say to me, Oh, you didn't tell me 615 00:36:35,760 --> 00:36:38,279 Speaker 3: you wanted the lowest interest, right, are you? 616 00:36:38,480 --> 00:36:38,560 Speaker 1: What? 617 00:36:39,320 --> 00:36:41,080 Speaker 2: Like? Isn't that like understood anyway? 618 00:36:41,120 --> 00:36:43,279 Speaker 3: I basically had to negotiate after they gave me a 619 00:36:43,280 --> 00:36:45,719 Speaker 3: crappy offer. I had to be like, no, you've got 620 00:36:45,719 --> 00:36:47,600 Speaker 3: to do better than that. But they did tell me 621 00:36:48,040 --> 00:36:52,600 Speaker 3: that they gave me my scores and they were close 622 00:36:52,760 --> 00:36:55,680 Speaker 3: to what credit Karma said, but not exact. 623 00:36:55,960 --> 00:36:57,480 Speaker 2: They were a little bit lower. 624 00:36:57,800 --> 00:37:02,560 Speaker 1: They're also different internal scores that financial institutions may use 625 00:37:03,160 --> 00:37:06,080 Speaker 1: to rank you that you will not generally see because 626 00:37:06,080 --> 00:37:09,560 Speaker 1: they're not for the public. But the Big Three that 627 00:37:09,640 --> 00:37:14,280 Speaker 1: triumvirt of credit has attempted to resolve some of their 628 00:37:14,400 --> 00:37:18,600 Speaker 1: larger discrepancies. So they joined forces and possied up with 629 00:37:18,680 --> 00:37:22,920 Speaker 1: a tech company to create a credit scoring algorithm. This 630 00:37:22,960 --> 00:37:27,040 Speaker 1: company's name Fair Isaac and Company. You may know them 631 00:37:27,080 --> 00:37:29,000 Speaker 1: by their street name Fiico. 632 00:37:29,600 --> 00:37:29,920 Speaker 2: That's orry. 633 00:37:29,920 --> 00:37:32,360 Speaker 3: I found that in nineteen fifty six, the firm already 634 00:37:32,400 --> 00:37:36,200 Speaker 3: been selling algorithms, selling the aforementioned to credit score algorithms 635 00:37:36,239 --> 00:37:41,160 Speaker 3: for decades when the Big Three started their quest for 636 00:37:41,400 --> 00:37:46,400 Speaker 3: industry domination and for standardization of credit scores. So the 637 00:37:46,440 --> 00:37:49,960 Speaker 3: results which hit the market in nineteen eighty nine was 638 00:37:50,280 --> 00:37:54,320 Speaker 3: very similar to the very algorithm we still see today. 639 00:37:54,400 --> 00:37:58,200 Speaker 3: And it spread because I would say, up to this point, 640 00:37:58,560 --> 00:38:00,759 Speaker 3: it was the best, It was the best one, it 641 00:38:00,800 --> 00:38:02,760 Speaker 3: was the most equitable one, it. 642 00:38:02,680 --> 00:38:06,360 Speaker 2: Was the least bad. Kay, fair enough, Yeah. 643 00:38:06,040 --> 00:38:12,040 Speaker 1: And this spreads like wildfire. Today, nearly everyone in America 644 00:38:12,200 --> 00:38:15,920 Speaker 1: has a codified financial identity of some sort. It's really 645 00:38:16,000 --> 00:38:19,480 Speaker 1: tough to live entirely off the grid. It's simply a 646 00:38:19,520 --> 00:38:24,080 Speaker 1: reality of American life, and history reminds us yet again, 647 00:38:24,400 --> 00:38:28,160 Speaker 1: as common as that seems now, credit scoring is not universal. 648 00:38:28,600 --> 00:38:32,120 Speaker 1: People in the past were definitely correct to worry about 649 00:38:32,120 --> 00:38:35,400 Speaker 1: the concentration of power in the hands of these secretive 650 00:38:35,480 --> 00:38:42,879 Speaker 1: private organizations. And it's nuts because we don't talk about 651 00:38:42,880 --> 00:38:46,800 Speaker 1: it often, but these credit companies had to regularly defend 652 00:38:46,840 --> 00:38:51,759 Speaker 1: themselves against charges of espionage. People even called them the 653 00:38:51,800 --> 00:38:55,640 Speaker 1: new inquisition at times for their snoopiness, their snoopy dupes. 654 00:38:55,719 --> 00:39:01,839 Speaker 1: But this is also still even now. It's a way 655 00:39:01,840 --> 00:39:05,680 Speaker 1: of maintaining social hierarchy, maybe not by design, but in practice, 656 00:39:05,760 --> 00:39:10,480 Speaker 1: because you can create these very dangerous, damaging feedback loops 657 00:39:10,480 --> 00:39:15,319 Speaker 1: of credit. Especially if you are a poorer American, your 658 00:39:15,440 --> 00:39:19,680 Speaker 1: lower credit score will translate to bigger down payments, higher 659 00:39:19,719 --> 00:39:23,480 Speaker 1: interest rates on purchases. It's expensive to be poor. And 660 00:39:23,520 --> 00:39:28,360 Speaker 1: then if you have this extra knock on strain added 661 00:39:28,400 --> 00:39:32,040 Speaker 1: to your household budget, it raises your likelihood of having 662 00:39:32,080 --> 00:39:35,200 Speaker 1: bankruptcy and defaulting on your loans. And what does that do, 663 00:39:35,560 --> 00:39:38,760 Speaker 1: ding ding ding? It lowers your credit score even more. 664 00:39:39,160 --> 00:39:42,239 Speaker 3: See I'm looking at my scores right now, TransUnion and Equifax. 665 00:39:42,280 --> 00:39:45,520 Speaker 3: Is what credit Karmas shows. My TransUnion score is up 666 00:39:45,640 --> 00:39:49,600 Speaker 3: nine points, but my Equifax score is down thirteen points. 667 00:39:49,920 --> 00:39:52,600 Speaker 3: It's like it's a mystery. And then there are things 668 00:39:52,600 --> 00:39:54,680 Speaker 3: that it can show. But why did it affect one 669 00:39:54,760 --> 00:39:57,239 Speaker 3: one way and the other another way? And we will 670 00:39:57,800 --> 00:39:59,680 Speaker 3: help you guys out with a little bit of a 671 00:39:59,800 --> 00:40:02,040 Speaker 3: de tail around how that FICO score? 672 00:40:02,320 --> 00:40:05,640 Speaker 1: See what change determined? Yeah, you and click see what changed? 673 00:40:05,680 --> 00:40:08,840 Speaker 1: And I'm a fan of credit harma as well. You 674 00:40:08,880 --> 00:40:11,160 Speaker 1: know they're not paying us to say this. This is just, 675 00:40:11,280 --> 00:40:14,919 Speaker 1: I think, a very accessible resource for those of us 676 00:40:15,040 --> 00:40:19,920 Speaker 1: who are lay folk, who are not wizards of financial formula, 677 00:40:20,080 --> 00:40:24,120 Speaker 1: nor sorcerers of something else that begins with an S 678 00:40:24,440 --> 00:40:25,319 Speaker 1: for the alliteration. 679 00:40:25,880 --> 00:40:28,440 Speaker 2: We'll keep it in like that boy, I'm under the 680 00:40:28,440 --> 00:40:32,600 Speaker 2: weather anyway. So is this imperfect? Absolutely? Is it potentially dangerous? 681 00:40:32,960 --> 00:40:33,680 Speaker 2: For sure? 682 00:40:34,880 --> 00:40:38,840 Speaker 1: But as ridiculous as the origins may be, the credit 683 00:40:38,840 --> 00:40:43,279 Speaker 1: score still fills a modern need because the alternative would 684 00:40:43,320 --> 00:40:45,319 Speaker 1: be going back to the days of old. You have 685 00:40:45,400 --> 00:40:49,440 Speaker 1: to rely on personal relationships. You need money for your business. 686 00:40:49,680 --> 00:40:53,239 Speaker 1: You better hope you're cool with the people in town 687 00:40:53,280 --> 00:40:57,719 Speaker 1: who already run everything, because they're the ones who approve you. Also, 688 00:40:57,760 --> 00:41:01,120 Speaker 1: credit scores give us a good read on society. Overall. 689 00:41:01,239 --> 00:41:05,720 Speaker 1: It helps us see the financial forest, you know. Credit 690 00:41:06,360 --> 00:41:09,600 Speaker 1: like large trends and credit reports are used to inform 691 00:41:09,680 --> 00:41:14,279 Speaker 1: decisions about housing and insurance, cost, utilities, et cetera, et cetera, 692 00:41:14,320 --> 00:41:18,680 Speaker 1: et cetera. So I don't know, I'd see ridiculous beginning, 693 00:41:19,320 --> 00:41:24,120 Speaker 1: still kind of a ridiculous thing, necessary evil. Maybe we 694 00:41:24,200 --> 00:41:28,160 Speaker 1: can end with a little bit of demystification, we did 695 00:41:28,200 --> 00:41:31,719 Speaker 1: find out some of the ratio of what makes a 696 00:41:31,760 --> 00:41:33,120 Speaker 1: FICO credit score. 697 00:41:33,480 --> 00:41:40,200 Speaker 3: Yes, unlike other credit reporting scoring methods of yesteryear, age, gender, 698 00:41:40,520 --> 00:41:43,600 Speaker 3: marital status, all of that, as we mentioned, no longer considered. 699 00:41:43,600 --> 00:41:46,799 Speaker 3: Instead we look at what they look at five factors 700 00:41:46,840 --> 00:41:50,040 Speaker 3: to calculate an individual's FYCO credit score, and maybe we 701 00:41:50,120 --> 00:41:53,840 Speaker 3: round robin this one. Then Number one, which can affect 702 00:41:54,000 --> 00:41:57,440 Speaker 3: thirty five as a thirty five percent effect on your 703 00:41:57,480 --> 00:42:01,040 Speaker 3: credit score is payment history. The anyone could probably think 704 00:42:01,040 --> 00:42:03,960 Speaker 3: that's probably the most important factor. If you're getting behind 705 00:42:03,960 --> 00:42:07,080 Speaker 3: on your payments, if you're defaulting on payments, getting collections 706 00:42:07,080 --> 00:42:08,879 Speaker 3: taken out against you, that is not going to be good. 707 00:42:08,880 --> 00:42:12,200 Speaker 3: So that is the number one factor of determining that 708 00:42:12,280 --> 00:42:15,960 Speaker 3: FICO score, whether or not you've paid past credit accounts 709 00:42:16,280 --> 00:42:16,800 Speaker 3: on time. 710 00:42:17,640 --> 00:42:21,040 Speaker 1: Number two, how much do you owe? That's thirty percent 711 00:42:21,080 --> 00:42:24,040 Speaker 1: of your grade. Here the total amount of credit and 712 00:42:24,160 --> 00:42:29,040 Speaker 1: loans you are currently using compared to your total all 713 00:42:29,160 --> 00:42:34,520 Speaker 1: in credit limit, counting all your cards, counting all the debts, 714 00:42:34,560 --> 00:42:38,719 Speaker 1: all the assets. This is known as your utilization rate 715 00:42:38,800 --> 00:42:44,880 Speaker 1: and real quick note her FICO scores. The typical score 716 00:42:45,000 --> 00:42:48,320 Speaker 1: ranges from three hundred to eight to fifty eight fifty 717 00:42:48,320 --> 00:42:50,000 Speaker 1: I was close. I said nine hundred. You can get 718 00:42:50,000 --> 00:42:53,759 Speaker 1: a nine hundred score with something called vantage score, but 719 00:42:53,960 --> 00:42:56,719 Speaker 1: not really with FICO. It's kind of like those professors 720 00:42:56,840 --> 00:42:59,480 Speaker 1: who and I know some of these professors who on 721 00:42:59,520 --> 00:43:01,520 Speaker 1: the first day the class tell you no one gets 722 00:43:01,520 --> 00:43:02,680 Speaker 1: an A in their class. 723 00:43:02,960 --> 00:43:05,200 Speaker 2: Oh what a flex don't care for that. I don't 724 00:43:05,280 --> 00:43:08,520 Speaker 2: love it. Yeah, I don't care for you. Number three length. 725 00:43:08,719 --> 00:43:10,920 Speaker 3: We've mentioned all of these in passing in one by 726 00:43:11,000 --> 00:43:13,680 Speaker 3: one point or another. I believe maybe minus number five, 727 00:43:13,719 --> 00:43:16,600 Speaker 3: which we'll get to. But number three length of credit history, 728 00:43:16,640 --> 00:43:19,800 Speaker 3: which has a fifteen percent impact on your FICO score, 729 00:43:19,800 --> 00:43:22,120 Speaker 3: And that says it all in the name the length 730 00:43:22,160 --> 00:43:25,279 Speaker 3: of time that you've held these various credit accounts, and 731 00:43:25,320 --> 00:43:28,160 Speaker 3: it's averaged. I was just talking off Mike about how 732 00:43:28,160 --> 00:43:33,080 Speaker 3: I was looking at mine. My average credit age is 733 00:43:33,280 --> 00:43:36,760 Speaker 3: five years, but that Bank of America card I mentioned earlier, 734 00:43:36,760 --> 00:43:40,000 Speaker 3: I've had for eighteen years. So it's an average of 735 00:43:40,040 --> 00:43:42,360 Speaker 3: all of the various accounts. So if you have a 736 00:43:42,480 --> 00:43:44,800 Speaker 3: very old account, but then you've applied for a bunch 737 00:43:45,080 --> 00:43:49,040 Speaker 3: in recent times, that will lower the average age of 738 00:43:49,040 --> 00:43:51,760 Speaker 3: your credit history. So do be careful there, but again 739 00:43:52,040 --> 00:43:55,000 Speaker 3: only a fifteen percent hit, so not a massive factor. 740 00:43:55,560 --> 00:43:58,399 Speaker 1: Number four and so if we go to number four, 741 00:43:58,480 --> 00:44:02,319 Speaker 1: this is one we hadn't mentioned. And as explicitly you 742 00:44:02,680 --> 00:44:06,840 Speaker 1: heard us talk about soft via hard inquiries on credit. 743 00:44:07,120 --> 00:44:10,160 Speaker 1: The reason that affects your credit score is because new 744 00:44:10,280 --> 00:44:14,960 Speaker 1: credit new credit is ten percent of your grade. And 745 00:44:15,080 --> 00:44:19,080 Speaker 1: new credit maybe a misleading term there. It's not just 746 00:44:19,160 --> 00:44:22,560 Speaker 1: how often you open new accounts, it's how often you 747 00:44:22,640 --> 00:44:25,680 Speaker 1: attempt to open new accounts, how often you apply for 748 00:44:25,800 --> 00:44:31,040 Speaker 1: something which creates that hard inquiry which helps the system understand, hey, 749 00:44:31,520 --> 00:44:35,000 Speaker 1: why is this person Johnny Blue Jeans out in Omaha? 750 00:44:35,320 --> 00:44:38,000 Speaker 1: Why did they just apply for ten credit cards? 751 00:44:38,120 --> 00:44:40,440 Speaker 3: Now that being said, ben I did find something out 752 00:44:41,000 --> 00:44:44,000 Speaker 3: in some research that if you are shopping around for 753 00:44:44,120 --> 00:44:48,640 Speaker 3: a major loan or a major mortgage type thing or 754 00:44:48,680 --> 00:44:52,600 Speaker 3: a car offer, a loan like that, you only get 755 00:44:52,719 --> 00:44:56,239 Speaker 3: hit like once. It does determine if you do it 756 00:44:56,239 --> 00:44:58,160 Speaker 3: within a period of time. I don't know exactly how 757 00:44:58,160 --> 00:45:00,840 Speaker 3: this is determined, but you're not gonna have all seven 758 00:45:00,920 --> 00:45:05,000 Speaker 3: of those hard inquiries stay on your account limited because 759 00:45:05,000 --> 00:45:06,400 Speaker 3: you shocked around and they understand that. 760 00:45:06,640 --> 00:45:08,200 Speaker 2: So I don't know exactly how that's determined. 761 00:45:09,040 --> 00:45:13,760 Speaker 3: You can report hard inquiries that are made in error 762 00:45:13,960 --> 00:45:17,560 Speaker 3: that you believe to these bureaus and potentially have them removed. 763 00:45:18,520 --> 00:45:19,239 Speaker 2: Good luck with that. 764 00:45:19,719 --> 00:45:23,360 Speaker 3: I would argue the bureaucracy behind these organizations is probably 765 00:45:23,400 --> 00:45:24,000 Speaker 3: pretty thick. 766 00:45:24,600 --> 00:45:25,040 Speaker 2: Yeah. 767 00:45:25,080 --> 00:45:29,720 Speaker 1: And it's also you know, the standing position for your 768 00:45:29,760 --> 00:45:33,600 Speaker 1: credit should be to have your credit frozen and unfreeze 769 00:45:33,640 --> 00:45:37,640 Speaker 1: it when you want. Anyway, here's number five. This is 770 00:45:38,800 --> 00:45:41,680 Speaker 1: an important point. They're not looking at just the amount 771 00:45:41,680 --> 00:45:45,120 Speaker 1: of debt to credit you have, not just your utilization, 772 00:45:45,560 --> 00:45:49,359 Speaker 1: but the kind of credit you have. Credit cards are 773 00:45:49,360 --> 00:45:53,360 Speaker 1: different from installment loans, which are different from mortgage loans. 774 00:45:53,560 --> 00:45:56,719 Speaker 1: So all of that goes into effect to keep this 775 00:45:56,840 --> 00:46:01,800 Speaker 1: crazy thing we call modern americanists society sort of afloat 776 00:46:01,920 --> 00:46:06,120 Speaker 1: until something goes wrong. I declare the verdict on credit 777 00:46:06,160 --> 00:46:11,120 Speaker 1: scores ridiculous. Oh necessary. I hate to say it, but 778 00:46:11,160 --> 00:46:12,040 Speaker 1: the last. 779 00:46:12,320 --> 00:46:15,120 Speaker 3: I completely agree. Yeah, and back to just really quickly, 780 00:46:15,160 --> 00:46:18,839 Speaker 3: back to that credit mix. That's interesting, isn't it. Ben 781 00:46:19,080 --> 00:46:22,879 Speaker 3: Like you get like a boost for how like it's 782 00:46:22,880 --> 00:46:25,319 Speaker 3: almost like having a portfolio, you know, because on the 783 00:46:25,320 --> 00:46:28,200 Speaker 3: one hand, people, if you're thinking about debt in one way, 784 00:46:29,120 --> 00:46:31,640 Speaker 3: it's bad nobody wants to be carrying a bunch of debt. 785 00:46:31,880 --> 00:46:34,719 Speaker 2: But when you get to a certain point financially in 786 00:46:34,760 --> 00:46:36,680 Speaker 2: your life, you want to carry debt. 787 00:46:37,080 --> 00:46:39,879 Speaker 3: It's good to carry a mortgage because it boosts your 788 00:46:39,920 --> 00:46:40,600 Speaker 3: credit score. 789 00:46:40,920 --> 00:46:42,479 Speaker 2: So like the idea of. 790 00:46:42,440 --> 00:46:43,960 Speaker 3: You know, some people will be like, oh, I should 791 00:46:43,960 --> 00:46:47,160 Speaker 3: I pay my house off early? The answer there by 792 00:46:47,200 --> 00:46:51,160 Speaker 3: some financial experts would be no, because then you're getting 793 00:46:51,239 --> 00:46:54,640 Speaker 3: that off of your credits report, and that's not good 794 00:46:54,640 --> 00:46:57,280 Speaker 3: for you long term. So to carry a large note 795 00:46:57,320 --> 00:46:59,560 Speaker 3: like that on your credit report is a good thing, 796 00:46:59,600 --> 00:47:02,440 Speaker 3: and it's that you're capable of making those payments as 797 00:47:02,480 --> 00:47:04,880 Speaker 3: opposed to you know, making one lump sum or whatever. 798 00:47:04,920 --> 00:47:06,560 Speaker 3: So again I'm not an expert here. This is what 799 00:47:06,600 --> 00:47:09,320 Speaker 3: I have been told, so your information may be different, 800 00:47:09,360 --> 00:47:11,600 Speaker 3: and please do let us know if I'm absolutely full 801 00:47:11,640 --> 00:47:11,960 Speaker 3: of crap. 802 00:47:11,960 --> 00:47:13,440 Speaker 2: But it's something that I was told. 803 00:47:13,480 --> 00:47:17,200 Speaker 1: And that credit mix up is ten percent of your 804 00:47:17,200 --> 00:47:19,759 Speaker 1: overall grade. Those are the big factors, and you can 805 00:47:19,800 --> 00:47:23,880 Speaker 1: see already they can interact with one another in quite 806 00:47:23,920 --> 00:47:27,600 Speaker 1: complex ways. Another another kind of debt that would be 807 00:47:27,719 --> 00:47:30,760 Speaker 1: under a special umbrella would be things like student loans, 808 00:47:31,080 --> 00:47:33,800 Speaker 1: you know what I mean, which are often already wrapped 809 00:47:33,920 --> 00:47:38,160 Speaker 1: up in governmental actions, so we hope this finds you 810 00:47:39,040 --> 00:47:41,680 Speaker 1: in good health and amid grand adventure. 811 00:47:41,719 --> 00:47:43,279 Speaker 2: Folks. Thanks for tuning in. 812 00:47:43,600 --> 00:47:47,440 Speaker 1: Thanks as always to our super producer mister Max Williams. 813 00:47:47,680 --> 00:47:50,359 Speaker 2: Thank you to Jonathan Strickland. 814 00:47:49,840 --> 00:47:55,399 Speaker 1: Aka the Quister aka the Ridiculous History Credit Correspondent. He's 815 00:47:55,440 --> 00:47:59,799 Speaker 1: coming to spy on you in a town nearby the RHCC. 816 00:48:00,800 --> 00:48:02,279 Speaker 2: That's what he is. That's right, Hughes. 817 00:48:02,280 --> 00:48:04,680 Speaker 3: Thanks Chris Frosciotis and Neves Jeff Coots here in spirit. 818 00:48:04,800 --> 00:48:07,400 Speaker 2: What do we say we got the quizzor well, how 819 00:48:07,440 --> 00:48:08,200 Speaker 2: about the puzzler? 820 00:48:08,239 --> 00:48:08,480 Speaker 1: A J. 821 00:48:08,600 --> 00:48:10,000 Speaker 2: Bahamas Jacobs Big thanks to. 822 00:48:09,960 --> 00:48:13,120 Speaker 1: You as well, Bud Big, Big thanks to Alex Williams 823 00:48:13,440 --> 00:48:17,000 Speaker 1: currently BiVO whacking out there in Austin. Safe travels, Bro, 824 00:48:17,120 --> 00:48:20,319 Speaker 1: You're an excellent composer. And Noel Big thanks to you. 825 00:48:20,920 --> 00:48:23,240 Speaker 2: And you as well. Let's see you next time, folks. 826 00:48:29,239 --> 00:48:33,040 Speaker 3: For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, 827 00:48:33,120 --> 00:48:35,280 Speaker 3: or wherever you listen to your favorite shows.