1 00:00:00,120 --> 00:00:01,600 Speaker 1: Please welcome to the stage. 2 00:00:01,760 --> 00:00:05,720 Speaker 2: Bank of America Chairman and CEO Brian moynihan, with Bloomberg's 3 00:00:05,760 --> 00:00:08,600 Speaker 2: David Weston. So, Brian, thank you so much for being here. 4 00:00:08,600 --> 00:00:10,080 Speaker 1: It's great to be here. They must have been expecting 5 00:00:10,119 --> 00:00:11,160 Speaker 1: some other people or something. 6 00:00:11,560 --> 00:00:13,640 Speaker 2: There's plenty of room if you want to invite some guests. 7 00:00:14,600 --> 00:00:17,640 Speaker 2: So two weeks ago we've been talking about the dead ceiling. 8 00:00:17,720 --> 00:00:19,880 Speaker 2: We don't have to talk about that anymore, but remember 9 00:00:19,880 --> 00:00:22,040 Speaker 2: we can talk about the aftermath of what we went 10 00:00:22,079 --> 00:00:24,239 Speaker 2: through there in Washington, and specifically, one of things that's 11 00:00:24,239 --> 00:00:27,080 Speaker 2: been covered on Bloomberg and elsewhere is obviously the government 12 00:00:27,080 --> 00:00:30,000 Speaker 2: coffers were drawn down pretty substantially as they came right 13 00:00:30,080 --> 00:00:32,320 Speaker 2: up to that so called X date, and now they've 14 00:00:32,320 --> 00:00:34,120 Speaker 2: got issue a lot of T bills. I saw one 15 00:00:34,479 --> 00:00:36,520 Speaker 2: like eight hundred and fifty million by the end. 16 00:00:36,400 --> 00:00:39,640 Speaker 1: Of the summer billion we worsh it, or million billion, eight. 17 00:00:39,880 --> 00:00:42,280 Speaker 2: Billion and maybe a trillion by the end of the year, 18 00:00:42,400 --> 00:00:46,680 Speaker 2: is what I saw. The speculations that might draw liquidity 19 00:00:46,720 --> 00:00:48,559 Speaker 2: out of the marketplace. Are you seeing that at Bank 20 00:00:48,600 --> 00:00:49,360 Speaker 2: of America yet? 21 00:00:49,840 --> 00:00:52,199 Speaker 3: Yeah, it's too strong because I think if you look 22 00:00:52,240 --> 00:00:54,560 Speaker 3: they've daily published their balances, and they moved up a 23 00:00:54,640 --> 00:00:57,880 Speaker 3: chunk and you guys covered us today and because of 24 00:00:58,000 --> 00:01:02,000 Speaker 3: tax payments coming in and you get estimate tax payments, 25 00:01:02,040 --> 00:01:05,360 Speaker 3: et cetera. But their policy has been by their own design, 26 00:01:05,400 --> 00:01:07,560 Speaker 3: to get to have a trillion dollars of money running 27 00:01:07,560 --> 00:01:09,360 Speaker 3: through this that they have at all given times, so 28 00:01:09,400 --> 00:01:10,840 Speaker 3: they have fund to keep it level there. So they're 29 00:01:10,840 --> 00:01:12,440 Speaker 3: gonna have to push up and do that kind of 30 00:01:12,440 --> 00:01:15,440 Speaker 3: funding level. You know, when I ask people, is this disruptive? 31 00:01:15,440 --> 00:01:17,959 Speaker 3: And not all the experts tell me yes and no 32 00:01:18,120 --> 00:01:19,880 Speaker 3: and yes. In the hand there's a lot of issuance, 33 00:01:19,920 --> 00:01:22,039 Speaker 3: but no everybody knew it was coming, and so it 34 00:01:22,080 --> 00:01:25,600 Speaker 3: may move sort of trading markets around, but fundamentally, the 35 00:01:25,680 --> 00:01:28,800 Speaker 3: idea of the government was gonna run out of money 36 00:01:29,080 --> 00:01:30,480 Speaker 3: was not something people were planning on. 37 00:01:30,560 --> 00:01:31,440 Speaker 1: So we'll see. 38 00:01:31,720 --> 00:01:34,160 Speaker 3: It's just another thing to worry about for the next 39 00:01:34,200 --> 00:01:34,720 Speaker 3: six months. 40 00:01:34,800 --> 00:01:36,560 Speaker 2: Does does it put any kind of a crimp in 41 00:01:36,600 --> 00:01:38,720 Speaker 2: your ability to lend? I mean money has to come 42 00:01:38,720 --> 00:01:39,200 Speaker 2: from somewhere. 43 00:01:39,680 --> 00:01:41,280 Speaker 3: Well, there's a lot of money sitting at the FED 44 00:01:41,319 --> 00:01:43,400 Speaker 3: and the of night repo facility and money funds has 45 00:01:43,480 --> 00:01:45,560 Speaker 3: just been put back, and so the dynamics of how 46 00:01:45,600 --> 00:01:48,640 Speaker 3: this all moves around is interesting. I don't think in 47 00:01:48,680 --> 00:01:51,040 Speaker 3: the worry b it took deposits out of the banking system, 48 00:01:51,160 --> 00:01:54,640 Speaker 3: But you know, there's I'm not sure people see that 49 00:01:54,640 --> 00:01:57,280 Speaker 3: as a big issue. And by the way, the Treasury Secretary, 50 00:01:57,320 --> 00:01:58,840 Speaker 3: I think they said today they will do this on 51 00:01:58,880 --> 00:02:02,560 Speaker 3: a non disruptive basis because they just ran down to 52 00:02:02,600 --> 00:02:04,240 Speaker 3: thirty nine billion. We're still able to pay the bills, 53 00:02:04,240 --> 00:02:05,960 Speaker 3: so they don't need to get there tomorrow, and they'll 54 00:02:05,960 --> 00:02:07,480 Speaker 3: build it up over time, but their goal is to 55 00:02:07,520 --> 00:02:08,799 Speaker 3: get back in a more regular way. 56 00:02:09,000 --> 00:02:10,399 Speaker 1: The best news about this whole. 57 00:02:10,200 --> 00:02:14,360 Speaker 3: Dialogue is they've got an agreement that extends a period 58 00:02:14,400 --> 00:02:16,480 Speaker 3: of time, so we shouldn't have to deal with this 59 00:02:16,560 --> 00:02:18,600 Speaker 3: for a while, which is really critical because the United 60 00:02:18,639 --> 00:02:20,720 Speaker 3: States has to be the beacon of stability strength in 61 00:02:20,760 --> 00:02:23,639 Speaker 3: the world, and at times when this discussion is going 62 00:02:23,800 --> 00:02:26,840 Speaker 3: on and you travel the world, everybody gets fixated on 63 00:02:26,919 --> 00:02:30,160 Speaker 3: it because United States is the benchmark of benchmarks, and 64 00:02:30,160 --> 00:02:33,120 Speaker 3: if it goes completely somehow accidentally, it's a real problem. 65 00:02:33,160 --> 00:02:35,440 Speaker 3: And so they get all fixated and all this sort 66 00:02:35,480 --> 00:02:37,840 Speaker 3: of activity in the planning for it. What would happen 67 00:02:37,919 --> 00:02:40,600 Speaker 3: in all our company? It just it would just be 68 00:02:40,639 --> 00:02:42,120 Speaker 3: better if it didn't go on. But it's a political 69 00:02:42,160 --> 00:02:43,040 Speaker 3: process and they. 70 00:02:43,320 --> 00:02:45,840 Speaker 2: Well beacon of stability. We didn't look much like that 71 00:02:45,880 --> 00:02:48,040 Speaker 2: for a little while there, Right, Do people forget about 72 00:02:48,080 --> 00:02:49,920 Speaker 2: that after it's over or are there lingering effects? 73 00:02:49,919 --> 00:02:53,400 Speaker 3: Do you think? Well, I think that look in my 74 00:02:53,960 --> 00:02:56,600 Speaker 3: memory in twenty eleven, it felt more serious at the 75 00:02:56,639 --> 00:02:59,040 Speaker 3: time than it did this time, because I think people 76 00:02:59,120 --> 00:03:00,880 Speaker 3: had learned from the actual shut down in some of 77 00:03:00,960 --> 00:03:04,800 Speaker 3: the dynamics of any actual downgraded United States that you 78 00:03:04,880 --> 00:03:09,280 Speaker 3: can't get that close. So I think the assured assurances 79 00:03:09,320 --> 00:03:14,320 Speaker 3: of both political parties plus the experts involved always was 80 00:03:14,360 --> 00:03:16,520 Speaker 3: we're not going to let this. It may be messy. 81 00:03:16,880 --> 00:03:18,760 Speaker 3: It's like a Greech tragedy. You always know what the 82 00:03:18,840 --> 00:03:20,000 Speaker 3: end is going to be. But it's fun to see 83 00:03:20,000 --> 00:03:21,440 Speaker 3: how they get there. But you know, the end of 84 00:03:21,480 --> 00:03:23,880 Speaker 3: the day, you know they got there, and that's important thing. 85 00:03:23,880 --> 00:03:26,600 Speaker 3: But it's a political process. There's another serious question about 86 00:03:26,600 --> 00:03:29,040 Speaker 3: how much debt the company, the country can afford, and 87 00:03:29,080 --> 00:03:31,280 Speaker 3: all that stuff. None of that's embedded in this as 88 00:03:31,360 --> 00:03:33,440 Speaker 3: much as it is. You know, the issue of the 89 00:03:33,440 --> 00:03:37,240 Speaker 3: moment was really getting the idea that somehow they would 90 00:03:37,280 --> 00:03:38,440 Speaker 3: not be able to pay their bills or have to 91 00:03:38,440 --> 00:03:40,440 Speaker 3: shut down and or to fall off the table, and 92 00:03:40,440 --> 00:03:41,240 Speaker 3: that's what's done. 93 00:03:41,600 --> 00:03:45,280 Speaker 2: Perhaps a related issue, actually, are the reserves required of 94 00:03:45,320 --> 00:03:47,640 Speaker 2: your bank and other banks? There are reports and I 95 00:03:47,640 --> 00:03:49,720 Speaker 2: don't think it's unexpected anybody that there's going to be 96 00:03:49,800 --> 00:03:53,240 Speaker 2: increase in capital requirements. If that goes forward, will it 97 00:03:53,360 --> 00:03:55,480 Speaker 2: have any effect on your ability to make loans? 98 00:03:56,400 --> 00:04:00,720 Speaker 3: Yeah, So there are multiple discussions which get sort of 99 00:04:01,440 --> 00:04:06,560 Speaker 3: pushed together. There's the standards for the final finalization of BASEL, 100 00:04:06,800 --> 00:04:09,600 Speaker 3: which is this broad set of things that's going on. 101 00:04:09,640 --> 00:04:12,000 Speaker 3: This stress test is going on that we forget about, 102 00:04:12,000 --> 00:04:14,440 Speaker 3: but that's going on also, and that result in some 103 00:04:14,560 --> 00:04:16,960 Speaker 3: surprises the industry in terms of capital. 104 00:04:16,520 --> 00:04:17,520 Speaker 1: Demands last year. 105 00:04:17,880 --> 00:04:22,800 Speaker 3: And then and then there's the question of applying standards 106 00:04:22,800 --> 00:04:25,240 Speaker 3: that apply to the g SIB banks, the biggest banks 107 00:04:25,240 --> 00:04:27,279 Speaker 3: broader in the platform because the size of some of 108 00:04:27,279 --> 00:04:29,320 Speaker 3: the banks, and so all three of those things get 109 00:04:29,400 --> 00:04:31,240 Speaker 3: mixed together a little bit. But you know, at the 110 00:04:31,320 --> 00:04:33,760 Speaker 3: end of the day, it's a fairly straightforward. If our 111 00:04:33,880 --> 00:04:37,479 Speaker 3: capital ratios go up by one hundred basis points, we basically, 112 00:04:37,800 --> 00:04:41,720 Speaker 3: you know, simply put, you can't make about one hundred 113 00:04:41,720 --> 00:04:45,840 Speaker 3: and fifty million dollars loans. And because people say, well, you 114 00:04:45,960 --> 00:04:47,520 Speaker 3: have more capital, make more loans. But if we took 115 00:04:47,600 --> 00:04:49,599 Speaker 3: risk on that capital, we wouldn't have that capital ratio. 116 00:04:49,640 --> 00:04:52,160 Speaker 3: So it has to be a riskless build. A capital 117 00:04:52,200 --> 00:04:54,160 Speaker 3: can't be out there taking risk. It's the only thing 118 00:04:54,160 --> 00:04:55,600 Speaker 3: you really do is leave it in cash or buy 119 00:04:55,600 --> 00:04:59,440 Speaker 3: treasury securities. And that's not a very productive use of 120 00:05:00,080 --> 00:05:04,000 Speaker 3: of money. So and if you had it, and that's 121 00:05:04,160 --> 00:05:06,080 Speaker 3: the problem. And so every time capital goes up, there's 122 00:05:06,080 --> 00:05:07,640 Speaker 3: a there's a countervailing effective. 123 00:05:07,680 --> 00:05:08,599 Speaker 1: It impacts lending. 124 00:05:08,920 --> 00:05:10,680 Speaker 2: Is that a gating function of here right now? Over 125 00:05:10,680 --> 00:05:12,320 Speaker 2: the last couple of weeks, you've been saying that in fact, 126 00:05:12,640 --> 00:05:14,560 Speaker 2: some of your lending is slowing down anyway, just because 127 00:05:14,600 --> 00:05:17,280 Speaker 2: the economy is slowing down. Is it a demand? Is 128 00:05:17,320 --> 00:05:19,719 Speaker 2: there demand enough for the loans that you can't make. 129 00:05:20,080 --> 00:05:22,320 Speaker 1: That ebbs and flows all the time. 130 00:05:22,400 --> 00:05:26,080 Speaker 3: So the loans, the loan demand is more product of 131 00:05:26,120 --> 00:05:29,760 Speaker 3: customer activity. And so our team has a recession predicted 132 00:05:30,440 --> 00:05:31,680 Speaker 3: beginning in the. 133 00:05:31,640 --> 00:05:33,280 Speaker 1: Third quarter, fourth quarter, first quarter. 134 00:05:33,560 --> 00:05:36,320 Speaker 3: Bank American Research team, which Candae Browning Plot leads, is 135 00:05:36,360 --> 00:05:39,840 Speaker 3: tremendous and they have that has moved out a little 136 00:05:39,839 --> 00:05:42,960 Speaker 3: bit as a consumer and activities stayed stronger even in 137 00:05:43,080 --> 00:05:46,280 Speaker 3: light of the fastest FED rate increase in long time. 138 00:05:46,720 --> 00:05:48,960 Speaker 1: And so but it's still the prediction. 139 00:05:49,080 --> 00:05:53,240 Speaker 3: And so I think companies, having gone through the inflation 140 00:05:53,360 --> 00:05:56,600 Speaker 3: and then it's sort of flattening out and thinking about 141 00:05:56,600 --> 00:05:59,000 Speaker 3: the future, just be more careful because they realize that 142 00:05:59,520 --> 00:06:01,919 Speaker 3: some we're able to move prices somewhere able to do it. 143 00:06:01,920 --> 00:06:04,800 Speaker 3: They're getting relief on the commodity side, on the price side, 144 00:06:04,800 --> 00:06:05,840 Speaker 3: but are they to be able to. 145 00:06:05,760 --> 00:06:06,560 Speaker 1: Hold price demands? 146 00:06:06,600 --> 00:06:08,640 Speaker 3: His final demand and the construction it just gonna be 147 00:06:08,640 --> 00:06:10,559 Speaker 3: a stronger year from now it is today and housing. 148 00:06:11,040 --> 00:06:13,200 Speaker 3: All this is on people's mind, so they tend to 149 00:06:13,440 --> 00:06:16,080 Speaker 3: pull in, and so that means line usage is flattened 150 00:06:16,080 --> 00:06:18,760 Speaker 3: back out. So line usage was here before the pandemic 151 00:06:18,800 --> 00:06:20,920 Speaker 3: and then fell and moved up, and it was kind 152 00:06:20,920 --> 00:06:23,039 Speaker 3: of moving up, you know, incrementally back to where the 153 00:06:23,040 --> 00:06:25,040 Speaker 3: pandemic sort of flatten out the last couple of months, 154 00:06:25,040 --> 00:06:28,039 Speaker 3: which means that your companies are just being a little 155 00:06:28,040 --> 00:06:28,520 Speaker 3: more careful. 156 00:06:28,760 --> 00:06:31,039 Speaker 2: So I see. Actually a survey was done in this 157 00:06:31,160 --> 00:06:33,560 Speaker 2: room of the likeli of recession in Q one of 158 00:06:33,640 --> 00:06:35,320 Speaker 2: twenty thw eighty four, and it looks like, what is 159 00:06:35,360 --> 00:06:37,760 Speaker 2: that sixty five percent of the people agree with your research? 160 00:06:37,800 --> 00:06:40,600 Speaker 2: Isn't that good to know you've got kind of ratification there. 161 00:06:40,880 --> 00:06:43,479 Speaker 3: One thing we always be careful if somebody educated me 162 00:06:43,520 --> 00:06:46,520 Speaker 3: once that the four projection recession by economists is always 163 00:06:46,520 --> 00:06:47,760 Speaker 3: like fifteen or twenty percent. 164 00:06:47,839 --> 00:06:49,960 Speaker 1: So anything above that means that they're convinced. 165 00:06:50,000 --> 00:06:53,680 Speaker 2: So so let's talk about something that's very much in 166 00:06:53,720 --> 00:06:57,680 Speaker 2: the news these days. That's artificial intelligence, particularly generative artificial intelligence, 167 00:06:57,880 --> 00:06:59,760 Speaker 2: large language model. You and I have talked in the 168 00:06:59,800 --> 00:07:02,800 Speaker 2: past about Erica, which is a form of I think 169 00:07:02,920 --> 00:07:05,799 Speaker 2: machine learning you've been using for some years now five years. 170 00:07:06,120 --> 00:07:07,720 Speaker 1: We've never really talked about what that is. 171 00:07:08,080 --> 00:07:10,400 Speaker 2: So it take us through what Erica is for Bank 172 00:07:10,440 --> 00:07:12,080 Speaker 2: of America right now, and then we can talk about 173 00:07:12,080 --> 00:07:12,600 Speaker 2: where it's going. 174 00:07:13,040 --> 00:07:17,640 Speaker 3: So what Erica is is a product capability that's in 175 00:07:17,680 --> 00:07:20,760 Speaker 3: the mobile banking app and other that you can go 176 00:07:20,880 --> 00:07:23,960 Speaker 3: into and type either type in or say, you know, 177 00:07:24,320 --> 00:07:28,000 Speaker 3: pay my landscape or pay my you know, school tuition, 178 00:07:28,120 --> 00:07:30,360 Speaker 3: whatever it is you know, and it will then say 179 00:07:31,080 --> 00:07:33,640 Speaker 3: pay and I'll say the name of the of the provider, 180 00:07:34,120 --> 00:07:35,760 Speaker 3: how much you want to pay and then it'll go 181 00:07:35,800 --> 00:07:37,720 Speaker 3: pay it. You'll just run through the bill paying system. 182 00:07:37,760 --> 00:07:39,360 Speaker 3: So instead of going the bill payment, going down the 183 00:07:39,360 --> 00:07:40,640 Speaker 3: list and do all stuff and. 184 00:07:40,640 --> 00:07:43,120 Speaker 1: Do it, or what's the routing number? What's my routing number? 185 00:07:43,160 --> 00:07:45,160 Speaker 3: Because that's a topic that people call us and ask 186 00:07:45,240 --> 00:07:46,400 Speaker 3: us about five million times a. 187 00:07:46,440 --> 00:07:47,200 Speaker 1: Year the US to call us. 188 00:07:47,240 --> 00:07:49,960 Speaker 3: Now they don't have the routing numbers on the base 189 00:07:50,000 --> 00:07:51,640 Speaker 3: of your check and routing number for all of you 190 00:07:51,720 --> 00:07:56,080 Speaker 3: is the same, so it's not but people call because frankly, 191 00:07:57,240 --> 00:07:59,120 Speaker 3: judging by the age of the people laughing, we were 192 00:07:59,120 --> 00:08:00,640 Speaker 3: taught some point or how to write a check and 193 00:08:00,680 --> 00:08:02,600 Speaker 3: how the numbers were and what was your account number. 194 00:08:03,320 --> 00:08:06,080 Speaker 3: That's no longer in the system, so when somebody's doing 195 00:08:06,120 --> 00:08:07,200 Speaker 3: ah and stuff. 196 00:08:07,000 --> 00:08:10,560 Speaker 1: So really, like seven or eight years ago, we. 197 00:08:10,480 --> 00:08:13,040 Speaker 3: Said, let's build something that can do that kind of 198 00:08:13,080 --> 00:08:17,320 Speaker 3: language processing the LP part of a thing and then 199 00:08:17,360 --> 00:08:20,680 Speaker 3: predict what the question was, use our data and our 200 00:08:20,720 --> 00:08:23,640 Speaker 3: information and come back with the answer to them. And 201 00:08:23,720 --> 00:08:25,240 Speaker 3: so we started to do that, and the first thing 202 00:08:25,240 --> 00:08:27,480 Speaker 3: we realized is the language that was out there for 203 00:08:27,520 --> 00:08:31,280 Speaker 3: these natural language recognition types of things was not written 204 00:08:31,320 --> 00:08:31,760 Speaker 3: for banking. 205 00:08:31,840 --> 00:08:34,040 Speaker 1: So what's my balance? Do you want to go to 206 00:08:34,080 --> 00:08:35,319 Speaker 1: a yoga class, you know. 207 00:08:36,400 --> 00:08:38,240 Speaker 3: It to think about it had So the first thing 208 00:08:38,240 --> 00:08:41,160 Speaker 3: I had to do is we went to outside and 209 00:08:41,200 --> 00:08:45,120 Speaker 3: got them to write a banking language program and things, 210 00:08:45,160 --> 00:08:46,560 Speaker 3: and then we had to pair it with our data 211 00:08:46,559 --> 00:08:50,080 Speaker 3: and our information. And so that's now five years old, 212 00:08:50,600 --> 00:08:52,640 Speaker 3: and you know, twenty million people use it and they 213 00:08:52,679 --> 00:08:54,160 Speaker 3: use one hundred and fifty two un million times. 214 00:08:54,400 --> 00:08:57,200 Speaker 1: We're just right across a billion interactions with it. 215 00:08:57,360 --> 00:08:59,440 Speaker 3: We'll get another billion in another twelve to eighteen months. 216 00:08:59,480 --> 00:09:02,520 Speaker 3: It's growing fast and it just saves a lot of 217 00:09:02,559 --> 00:09:04,080 Speaker 3: time for the customer and client, and. 218 00:09:04,520 --> 00:09:05,400 Speaker 1: The experience is great. 219 00:09:05,760 --> 00:09:08,040 Speaker 3: And yet people I think we'll start using even more 220 00:09:08,080 --> 00:09:10,200 Speaker 3: now because they're playing around with chat to ept and 221 00:09:10,200 --> 00:09:12,320 Speaker 3: doing other things that this was sort of foreign to them. 222 00:09:12,360 --> 00:09:15,079 Speaker 1: There what is Erica? Like you asked? But what we've 223 00:09:15,120 --> 00:09:16,880 Speaker 1: seen is it just continues. 224 00:09:16,440 --> 00:09:19,760 Speaker 3: To grow and you know, twenty percent interactions year every 225 00:09:19,800 --> 00:09:22,440 Speaker 3: year to thirty percent just people going because people like it, 226 00:09:22,720 --> 00:09:24,559 Speaker 3: use it more and more and more, and it can 227 00:09:24,600 --> 00:09:28,120 Speaker 3: answer now what the flip side of this is, why 228 00:09:28,880 --> 00:09:31,480 Speaker 3: have we deployed it? When you hear all the worries 229 00:09:31,520 --> 00:09:34,240 Speaker 3: you have about it, This is our data, This is 230 00:09:34,280 --> 00:09:38,440 Speaker 3: our processing. This is our predictive language artificial intelligence tool, 231 00:09:38,480 --> 00:09:41,960 Speaker 3: the virtual assistant, feeding off of our Q and a's 232 00:09:42,040 --> 00:09:43,920 Speaker 3: and questions that we've edited to make sure they're right, 233 00:09:44,000 --> 00:09:44,280 Speaker 3: so we. 234 00:09:44,240 --> 00:09:46,040 Speaker 1: Don't have the problems. 235 00:09:45,640 --> 00:09:48,400 Speaker 3: That we're dealing with everybody's data and everybody's answers and 236 00:09:48,640 --> 00:09:50,360 Speaker 3: been trying to figure out what is a perfect good 237 00:09:50,400 --> 00:09:53,360 Speaker 3: going to apply and so that controlled environment isn't the 238 00:09:53,440 --> 00:09:55,640 Speaker 3: environment that these wonderful. 239 00:09:55,400 --> 00:09:56,120 Speaker 1: Things are running on. 240 00:09:56,640 --> 00:09:58,840 Speaker 3: At some point they'll come together in know wards, why 241 00:09:58,840 --> 00:10:00,680 Speaker 3: do we have a proprietary one when you can use it, 242 00:10:00,679 --> 00:10:03,120 Speaker 3: but you've in between them is the ability to make 243 00:10:03,160 --> 00:10:06,080 Speaker 3: sure your data doesn't get pulled into places that shouldn't be, 244 00:10:06,160 --> 00:10:08,920 Speaker 3: making sure it works appropriately on your systems. And you're 245 00:10:08,960 --> 00:10:11,480 Speaker 3: reading the articles and your times and stuff you know 246 00:10:11,520 --> 00:10:13,760 Speaker 3: about in the healthcare industry or something written up and 247 00:10:13,760 --> 00:10:15,439 Speaker 3: all this, you know, all these pluses and minus this 248 00:10:15,520 --> 00:10:18,400 Speaker 3: thele case everybody's written about and all this stuff. You know, 249 00:10:18,640 --> 00:10:21,440 Speaker 3: those are the risks, and even the people really know this. 250 00:10:21,920 --> 00:10:23,960 Speaker 3: You'll tell you this is three to five years of 251 00:10:24,000 --> 00:10:26,319 Speaker 3: work to get those vegguaros build a system. 252 00:10:26,440 --> 00:10:27,520 Speaker 1: But we see it today. 253 00:10:27,520 --> 00:10:29,719 Speaker 3: Now you took that same stuff and you went and 254 00:10:29,720 --> 00:10:33,560 Speaker 3: applied it to analyze who the best customers to call 255 00:10:33,640 --> 00:10:36,960 Speaker 3: based on our data by people making inquiries to it 256 00:10:37,040 --> 00:10:39,800 Speaker 3: for prospects in our business banking, and it's called Banker 257 00:10:39,800 --> 00:10:42,080 Speaker 3: Assist and that is out there operating every day. So 258 00:10:42,240 --> 00:10:44,760 Speaker 3: a person has, you know, one hundred prospects are looking 259 00:10:44,760 --> 00:10:47,920 Speaker 3: at that in that line of business, and they can't 260 00:10:47,920 --> 00:10:49,440 Speaker 3: call one hundred all once, so it tells them the 261 00:10:49,440 --> 00:10:52,559 Speaker 3: best ten based on who they cover, what they do, 262 00:10:53,440 --> 00:10:56,000 Speaker 3: the kinds of industries they cover, the activity in the industry. 263 00:10:56,080 --> 00:10:56,280 Speaker 1: Look. 264 00:10:56,320 --> 00:10:58,360 Speaker 3: And these are fifty million underrepid companies, so this is 265 00:10:58,400 --> 00:11:01,679 Speaker 3: not huge companies and doing search of the outside environment 266 00:11:01,679 --> 00:11:03,520 Speaker 3: and talking about it. So we use it in other 267 00:11:03,559 --> 00:11:07,120 Speaker 3: places already. It's got high potential. It just we've got 268 00:11:07,160 --> 00:11:09,920 Speaker 3: to make sure we maintain the appropriate customer experience of 269 00:11:10,000 --> 00:11:11,760 Speaker 3: POPE control on the around it to make it work. 270 00:11:11,760 --> 00:11:13,680 Speaker 2: We'll talk about the control specific because one way to 271 00:11:13,679 --> 00:11:15,680 Speaker 2: expand it is just get more usage of it, which 272 00:11:15,679 --> 00:11:18,400 Speaker 2: you're doing right now. Another way is how to do 273 00:11:18,480 --> 00:11:20,640 Speaker 2: more things. And we had a report actually on the 274 00:11:20,640 --> 00:11:23,600 Speaker 2: Bloomberg this week about the CFPV as we're Financial Protection 275 00:11:23,640 --> 00:11:26,840 Speaker 2: Bureau coming out and warning banks saying, watch this generative 276 00:11:26,840 --> 00:11:29,760 Speaker 2: AI banks because it can make some mistakes. How do 277 00:11:29,800 --> 00:11:32,000 Speaker 2: you make sure that you can use it the right 278 00:11:32,040 --> 00:11:33,360 Speaker 2: way and you don't go too far it. 279 00:11:33,720 --> 00:11:36,440 Speaker 3: So that's the constraint on the openness of it, for 280 00:11:36,520 --> 00:11:40,160 Speaker 3: lack of better term, by constraining it to our data 281 00:11:40,160 --> 00:11:42,840 Speaker 3: and our information and editing and looking at the answers 282 00:11:42,840 --> 00:11:46,000 Speaker 3: it gives and constantly making sure. But that's easier when 283 00:11:46,040 --> 00:11:48,920 Speaker 3: you're saying it's our customer base, our data, our systems, 284 00:11:48,920 --> 00:11:51,840 Speaker 3: our information, and our transactions. I'm not trying to assess 285 00:11:51,840 --> 00:11:55,040 Speaker 3: the whole everything out there, and that's a whole different question. 286 00:11:55,160 --> 00:11:57,760 Speaker 3: So and that's but that's the lesson learned. But that 287 00:11:57,920 --> 00:12:00,120 Speaker 3: environment doesn't do a lot of good for society. It 288 00:12:00,120 --> 00:12:01,800 Speaker 3: does a lot of good for Bank of America customers 289 00:12:01,840 --> 00:12:04,240 Speaker 3: and Bank of America. The question to make a good society, 290 00:12:04,280 --> 00:12:05,520 Speaker 3: it's got to have all of it in there. And 291 00:12:05,520 --> 00:12:07,120 Speaker 3: that's the bridge from here to there. 292 00:12:07,000 --> 00:12:07,559 Speaker 1: Is that question. 293 00:12:07,679 --> 00:12:11,520 Speaker 3: So you know, the simple answers we monitor that. We 294 00:12:11,600 --> 00:12:13,720 Speaker 3: build it proprietarily, so it was built. 295 00:12:13,559 --> 00:12:14,360 Speaker 1: Around our. 296 00:12:15,880 --> 00:12:18,199 Speaker 3: Effectively the same thing a human would have done if 297 00:12:18,200 --> 00:12:21,360 Speaker 3: asked the question. And then you know, we convert that 298 00:12:21,400 --> 00:12:22,240 Speaker 3: and we back test it. 299 00:12:22,240 --> 00:12:23,640 Speaker 1: You have to always look at it. We don't. 300 00:12:23,920 --> 00:12:28,199 Speaker 3: We underwrite with automated and artificial intelligence and automated tools, 301 00:12:28,480 --> 00:12:30,719 Speaker 3: but not in an open environment like this where the 302 00:12:30,720 --> 00:12:33,439 Speaker 3: customer can keep that. It's much more controlled as to 303 00:12:33,520 --> 00:12:34,120 Speaker 3: how that works. 304 00:12:34,679 --> 00:12:37,760 Speaker 2: One of the hallmarks of your administration as CEO of 305 00:12:37,760 --> 00:12:40,520 Speaker 2: Bank of America has been controlling the costs that You've 306 00:12:40,520 --> 00:12:43,000 Speaker 2: been very adamant about that and very diligent about it throughout. 307 00:12:43,559 --> 00:12:45,240 Speaker 2: What levere do you have at this point? Is AI 308 00:12:45,520 --> 00:12:47,199 Speaker 2: a lever to help control costs? 309 00:12:47,240 --> 00:12:51,240 Speaker 3: Do you think, yeah, it will be, and it will 310 00:12:51,240 --> 00:12:54,720 Speaker 3: be another I always think of these things in arcs, 311 00:12:54,880 --> 00:12:57,160 Speaker 3: you know, So what could you do now that will 312 00:12:57,160 --> 00:13:01,480 Speaker 3: pay back over time and keep paying back? So if 313 00:13:01,480 --> 00:13:04,320 Speaker 3: you started out big fundamental things we made before people 314 00:13:04,360 --> 00:13:06,560 Speaker 3: knew what the cloud was, we built an internal cloud. 315 00:13:06,600 --> 00:13:09,719 Speaker 3: What that did is took all the server environments which 316 00:13:09,760 --> 00:13:12,280 Speaker 3: were inefficiently disposed of and that's the theory of a cloud, 317 00:13:12,480 --> 00:13:15,520 Speaker 3: and pushed them together and said you may want, you know, 318 00:13:15,600 --> 00:13:17,400 Speaker 3: green servers, but you're going to take the blue ones 319 00:13:17,640 --> 00:13:19,800 Speaker 3: because your software operated on them. Because you had somebody 320 00:13:19,840 --> 00:13:21,400 Speaker 3: deciding that green ones were better than blue one. So 321 00:13:21,480 --> 00:13:24,080 Speaker 3: we pushed that in. In that then you went to 322 00:13:24,120 --> 00:13:27,120 Speaker 3: the cloud. And then so these arcs of movement are 323 00:13:27,200 --> 00:13:29,200 Speaker 3: just so. Then you went to the cloud with parts 324 00:13:29,200 --> 00:13:30,880 Speaker 3: of your stuff, and then questions what can you do more? 325 00:13:30,960 --> 00:13:32,880 Speaker 3: And each of that pays you back over and over 326 00:13:32,920 --> 00:13:35,000 Speaker 3: again and so. But the first answer was to go 327 00:13:35,040 --> 00:13:37,440 Speaker 3: from thirty data centers to five or six and then 328 00:13:37,480 --> 00:13:39,800 Speaker 3: out of two or three, and then you have to 329 00:13:39,800 --> 00:13:40,679 Speaker 3: have a certain amount of it. 330 00:13:40,760 --> 00:13:42,040 Speaker 1: And that saved. 331 00:13:41,800 --> 00:13:43,480 Speaker 3: Us, you know, four or five hundred million dollars a 332 00:13:43,520 --> 00:13:45,360 Speaker 3: run rate expense a year. To give you a sense, 333 00:13:45,360 --> 00:13:45,920 Speaker 3: I mean, those. 334 00:13:45,760 --> 00:13:47,880 Speaker 1: Are big moves and all. 335 00:13:48,440 --> 00:13:51,120 Speaker 3: And then now the public cloud and et cetera, if 336 00:13:51,160 --> 00:13:53,200 Speaker 3: you think about it, so you have to think about 337 00:13:53,200 --> 00:13:54,000 Speaker 3: all the costs that way. 338 00:13:54,040 --> 00:13:55,080 Speaker 1: So in real estate, we. 339 00:13:55,000 --> 00:13:57,400 Speaker 3: Had one hundred and twenty odd million square feet of 340 00:13:57,440 --> 00:14:00,040 Speaker 3: real estate when the management team started twenty ten, and 341 00:14:00,120 --> 00:14:03,080 Speaker 3: we're down to sixty to sixty five or seventy. And 342 00:14:03,120 --> 00:14:07,280 Speaker 3: we still with all packing and stacking and work rules 343 00:14:07,320 --> 00:14:10,600 Speaker 3: even come the pandemic got about eighty percent, so there's a. 344 00:14:10,600 --> 00:14:11,120 Speaker 1: Lot of them to go. 345 00:14:11,240 --> 00:14:13,439 Speaker 3: Now with all the work rules and stuff, you have 346 00:14:13,480 --> 00:14:15,920 Speaker 3: a whole other round to go. And so all that 347 00:14:16,080 --> 00:14:18,280 Speaker 3: is you just manage expensive by looking at all work 348 00:14:18,320 --> 00:14:20,120 Speaker 3: that can go away, all in an efficiency go away, 349 00:14:20,120 --> 00:14:21,280 Speaker 3: and how you apply technology. 350 00:14:21,280 --> 00:14:22,600 Speaker 1: And then you got to the customers use it. 351 00:14:22,640 --> 00:14:26,840 Speaker 3: So seventy five percent of people our age bracket and 352 00:14:26,880 --> 00:14:32,880 Speaker 3: above use digital today. That number is growing the fastest 353 00:14:32,920 --> 00:14:36,600 Speaker 3: growing segment we have. It's not surprising that millennials in 354 00:14:36,760 --> 00:14:39,720 Speaker 3: gen z and et cetera have a higher representation. But 355 00:14:40,080 --> 00:14:42,480 Speaker 3: the reality is is it's not getting everybody use it's 356 00:14:42,480 --> 00:14:45,760 Speaker 3: also getting everybody use it fully. So even with younger 357 00:14:45,800 --> 00:14:48,920 Speaker 3: people who use it a lot, still will deposit or 358 00:14:49,000 --> 00:14:52,240 Speaker 3: checks at the branch. You know that difference is five 359 00:14:52,280 --> 00:14:56,360 Speaker 3: dollars fifty cents in a nickel depending on branch, hand 360 00:14:56,360 --> 00:14:58,320 Speaker 3: it to a teller, put it in ATM or do 361 00:14:58,360 --> 00:14:58,880 Speaker 3: another thing. 362 00:14:58,960 --> 00:15:01,000 Speaker 1: And so there's a migration. 363 00:15:01,080 --> 00:15:02,920 Speaker 3: Even with people you think or use it, they still 364 00:15:03,760 --> 00:15:05,880 Speaker 3: we still have a quarter billion dollars. We'll go out 365 00:15:05,880 --> 00:15:07,760 Speaker 3: of the ATMs by tomorrow this time. I mean it's 366 00:15:07,800 --> 00:15:10,440 Speaker 3: it's you know, it's amazing. So how do you get usage? 367 00:15:10,440 --> 00:15:12,720 Speaker 3: So it's all those techniques applied, applied and well AI 368 00:15:12,880 --> 00:15:16,320 Speaker 3: helped that, yes, because we have fourteen thousand colle agents. 369 00:15:16,320 --> 00:15:19,200 Speaker 1: We have where I really think is near. 370 00:15:19,080 --> 00:15:24,200 Speaker 3: Term helpful is in computer development, program development, and that 371 00:15:24,760 --> 00:15:27,200 Speaker 3: open source coding and analyzing. You still have to test it, 372 00:15:27,240 --> 00:15:30,360 Speaker 3: but you can get a written faster, make it, make 373 00:15:30,400 --> 00:15:33,160 Speaker 3: it simple for people, and that we think has great applications, 374 00:15:33,160 --> 00:15:33,840 Speaker 3: sooner random later. 375 00:15:34,040 --> 00:15:35,800 Speaker 2: Well, when we talked about costs, what about headcount. I 376 00:15:35,800 --> 00:15:37,360 Speaker 2: know you said that it's not so much your laying 377 00:15:37,400 --> 00:15:39,600 Speaker 2: officer's not hiring as many as you were a year ago. 378 00:15:39,880 --> 00:15:42,800 Speaker 3: This goes back to economic when I even talked to customers. 379 00:15:42,960 --> 00:15:46,160 Speaker 3: So last May we hired thirty three thousand people. Excuse me, 380 00:15:46,360 --> 00:15:48,600 Speaker 3: this May, we hired you know, six seven hundred, and 381 00:15:48,680 --> 00:15:50,920 Speaker 3: that's all because the turnover rate fell because last year 382 00:15:50,920 --> 00:15:52,840 Speaker 3: we're in the middle of great recognation and now it's 383 00:15:52,880 --> 00:15:56,320 Speaker 3: completely different. So we went from twelve percent turnover in 384 00:15:56,360 --> 00:15:58,200 Speaker 3: a company, which is sort of the long term level 385 00:15:58,240 --> 00:16:00,520 Speaker 3: we're at pre pandemic, down to six, up fifteen, and 386 00:16:00,640 --> 00:16:02,440 Speaker 3: now back down getting close to six. 387 00:16:02,520 --> 00:16:03,760 Speaker 1: So we don't have to hire. 388 00:16:03,600 --> 00:16:05,360 Speaker 3: As many people, yet we keep managed to head count 389 00:16:05,440 --> 00:16:08,960 Speaker 3: down will be We just had twenty five hundred wonderful 390 00:16:08,960 --> 00:16:11,880 Speaker 3: interns start this week, so that that'll make us about 391 00:16:12,000 --> 00:16:14,560 Speaker 3: by the time across the quarter in about two fifteen, 392 00:16:14,640 --> 00:16:17,280 Speaker 3: but you take them out will be two thirteen, two thirteen. 393 00:16:16,960 --> 00:16:19,280 Speaker 1: And a half or something like that, down from. 394 00:16:19,040 --> 00:16:21,720 Speaker 3: Two sixteen to seventeen a year end, and we've peaked 395 00:16:21,720 --> 00:16:25,240 Speaker 3: about two hundred nineteen thousand, not two hundred ninth. 396 00:16:25,320 --> 00:16:27,000 Speaker 2: More broadly, do you think the job market is a 397 00:16:27,040 --> 00:16:28,960 Speaker 2: bit softer than what the FED realizes because a lot 398 00:16:28,960 --> 00:16:30,120 Speaker 2: of their numbers are backward looking. 399 00:16:30,240 --> 00:16:30,880 Speaker 1: Yeah, I think. 400 00:16:31,080 --> 00:16:33,880 Speaker 3: I think if you talk to employers today in a 401 00:16:33,960 --> 00:16:38,520 Speaker 3: technology spaces there's always specialized things like welders in certain 402 00:16:38,520 --> 00:16:42,880 Speaker 3: businesses and high and manufacturing help explosion that was just 403 00:16:42,920 --> 00:16:47,600 Speaker 3: having been out there. But in general it's much less 404 00:16:47,800 --> 00:16:50,200 Speaker 3: tight than it was in the spot market, and that's 405 00:16:50,200 --> 00:16:51,760 Speaker 3: why the curt rates going down. 406 00:16:51,800 --> 00:16:52,280 Speaker 1: All that stuff. 407 00:16:52,320 --> 00:16:54,440 Speaker 3: You see it in the amount of hires and you know, 408 00:16:54,480 --> 00:16:58,080 Speaker 3: so job postings are still high. I'm not sure CEOs 409 00:16:58,080 --> 00:17:00,000 Speaker 3: that I talked to are pushing people to fill those 410 00:17:00,120 --> 00:17:02,760 Speaker 3: as much as film when you have to, and that 411 00:17:02,760 --> 00:17:05,600 Speaker 3: that has a dampening effect on the labor market that 412 00:17:05,600 --> 00:17:08,960 Speaker 3: won't show up any aggrea. Employment still at you know, 413 00:17:09,000 --> 00:17:11,359 Speaker 3: three point seven percent of employment is still very strong. 414 00:17:11,520 --> 00:17:14,639 Speaker 3: And so the big debate when if you want to 415 00:17:14,680 --> 00:17:16,200 Speaker 3: drive your ECONO is crazy, say, how can you have 416 00:17:16,240 --> 00:17:19,200 Speaker 3: an unemployment list recession? And you know they can't quite 417 00:17:19,240 --> 00:17:21,199 Speaker 3: get there, and that's kind of the interesting question. And 418 00:17:21,240 --> 00:17:25,320 Speaker 3: so even the highest predictors of unemployment don't even get 419 00:17:25,359 --> 00:17:28,080 Speaker 3: the five percent, which is hard to square. 420 00:17:28,480 --> 00:17:32,679 Speaker 2: One. Last one, another wholemark for you has been responsible 421 00:17:32,680 --> 00:17:34,920 Speaker 2: growth for back of America. From the day you took 422 00:17:34,920 --> 00:17:37,600 Speaker 2: over thiss what you said you were pursuing. And you've 423 00:17:37,600 --> 00:17:42,560 Speaker 2: also been fairly explicit about ESG investing and how that fits. 424 00:17:42,960 --> 00:17:46,240 Speaker 2: There's been some political turmoil about that in the country 425 00:17:46,240 --> 00:17:48,639 Speaker 2: now for some of the states now, including some effecting 426 00:17:48,640 --> 00:17:50,959 Speaker 2: in the Bank of America. How do you put together 427 00:17:51,320 --> 00:17:54,200 Speaker 2: your desire for responsible growth. On the one hand, we're 428 00:17:54,200 --> 00:17:57,360 Speaker 2: taking into account things like environmental and social and governmance. 429 00:17:57,760 --> 00:18:00,439 Speaker 3: As we look out, the oldest part of company has 430 00:18:00,440 --> 00:18:01,880 Speaker 3: been around for two hundred and thirty years. 431 00:18:01,960 --> 00:18:04,000 Speaker 1: Now they're almost in pushing beyond that. 432 00:18:04,119 --> 00:18:08,159 Speaker 3: And so you know, our industry is a product of 433 00:18:08,200 --> 00:18:11,000 Speaker 3: the communities that operates, and so we make it pretty simple. 434 00:18:11,720 --> 00:18:13,520 Speaker 1: We think of who we have to do a great job. 435 00:18:13,680 --> 00:18:14,600 Speaker 1: We have to do a great job. 436 00:18:14,480 --> 00:18:16,280 Speaker 3: For our customers, we have to do a great job 437 00:18:16,320 --> 00:18:17,680 Speaker 3: for our teammates, because we have to be the best 438 00:18:17,680 --> 00:18:19,520 Speaker 3: place for teammates to work, so we have the talent. 439 00:18:19,920 --> 00:18:21,520 Speaker 3: We have to do a great job for our shareholders. 440 00:18:21,520 --> 00:18:24,480 Speaker 3: We just had record operating profits in the first quarter, 441 00:18:24,520 --> 00:18:25,679 Speaker 3: and we have to do a great job for our 442 00:18:25,680 --> 00:18:28,600 Speaker 3: communities because frankly, a bank reflects the economy, and so 443 00:18:28,640 --> 00:18:30,679 Speaker 3: if the communities aren't strong, we're not gonna be strong. 444 00:18:30,760 --> 00:18:32,359 Speaker 1: So that's how we run the company. 445 00:18:32,400 --> 00:18:34,840 Speaker 3: And Responsible Growth really talks about that a little differently, 446 00:18:34,880 --> 00:18:36,040 Speaker 3: but that's how we run the company. 447 00:18:36,080 --> 00:18:37,959 Speaker 1: And that's but we have. 448 00:18:38,160 --> 00:18:41,120 Speaker 3: It's the genius of the and profits and purpose, not one. 449 00:18:41,080 --> 00:18:41,560 Speaker 1: Or the other. 450 00:18:41,640 --> 00:18:44,280 Speaker 3: And Jim Collins wrote about that in nineteen ninety six. 451 00:18:44,400 --> 00:18:46,760 Speaker 3: Is the theory behind you know, there's the thought process 452 00:18:46,760 --> 00:18:50,080 Speaker 3: behind it. That's all good stuff. And the answer is 453 00:18:50,119 --> 00:18:52,120 Speaker 3: sort of what's wrong with that? You know, thinking about 454 00:18:52,119 --> 00:18:54,200 Speaker 3: how to how to do a great job for my customers, 455 00:18:54,240 --> 00:18:56,159 Speaker 3: how to do a great job for my team, how 456 00:18:56,200 --> 00:18:58,240 Speaker 3: do I do a great job for our shareholders, how 457 00:18:58,280 --> 00:18:59,920 Speaker 3: do we do a great job for our communities? 458 00:19:00,080 --> 00:19:00,560 Speaker 2: What you know? 459 00:19:00,560 --> 00:19:03,119 Speaker 3: And that's so you know, when you think about the 460 00:19:03,359 --> 00:19:05,080 Speaker 3: energy and stuff, you know, go out to North Kota 461 00:19:05,160 --> 00:19:05,919 Speaker 3: with Senator Kramer. 462 00:19:05,960 --> 00:19:06,680 Speaker 1: We talked to people. 463 00:19:06,720 --> 00:19:08,639 Speaker 3: They got a net zero commitment at the state level 464 00:19:08,640 --> 00:19:11,320 Speaker 3: and everything, and they got all this innovation to do 465 00:19:11,400 --> 00:19:12,399 Speaker 3: carbon capture storage. 466 00:19:12,440 --> 00:19:14,240 Speaker 1: Our job is to help lean into that and make 467 00:19:14,280 --> 00:19:14,760 Speaker 1: that happen. 468 00:19:14,840 --> 00:19:17,760 Speaker 3: Well, we have energy for everybody, and good cheap energy 469 00:19:17,760 --> 00:19:19,720 Speaker 3: for everybody, and energy for people in the global South 470 00:19:19,760 --> 00:19:22,399 Speaker 3: that don't have this. You know, these are interesting questions, 471 00:19:22,400 --> 00:19:26,119 Speaker 3: but at the end of the day is private sector 472 00:19:26,160 --> 00:19:30,200 Speaker 3: will drive this, and the money's there and the talent's there, 473 00:19:30,240 --> 00:19:31,720 Speaker 3: and we had to drive it because at the end 474 00:19:31,720 --> 00:19:33,199 Speaker 3: of the day, we can make it happen and have 475 00:19:33,320 --> 00:19:36,440 Speaker 3: good growth and frankly from the United States perspective, dominate 476 00:19:36,480 --> 00:19:36,800 Speaker 3: the place. 477 00:19:37,280 --> 00:19:38,960 Speaker 2: Brian, thank you so much. Always great to take it that. 478 00:19:39,080 --> 00:19:42,000 Speaker 2: Brian money And is the chair and CEEO Bank of America.