WEBVTT 00:00:00.000 --> 00:00:03.500 Imagine possessing an invisible score that, you 00:00:03.500 --> 00:00:05.660 know, it follows you everywhere. It's a number 00:00:05.660 --> 00:00:07.580 that decides if you get a home loan, how much 00:00:07.580 --> 00:00:10.919 you pay for auto insurance and well, in some 00:00:10.919 --> 00:00:13.160 cases, whether you even qualify for a job. It's 00:00:13.160 --> 00:00:14.880 basically your financial fingerprint. Exactly. 00:00:14.939 --> 00:00:18.579 And today we are pulling back the curtain on 00:00:18.579 --> 00:00:22.679 the institutions that create, maintain and and 00:00:22.679 --> 00:00:24.960 wield that score. We are diving deep into the 00:00:24.960 --> 00:00:28.140 world of the credit bureau. This is a a powerful. 00:00:28.719 --> 00:00:32.420 globally adopted and frankly, often opaque system. 00:00:32.460 --> 00:00:35.020 Right. We've compiled a stack of sources, articles, 00:00:35.320 --> 00:00:38.039 regulatory documents, historical analyses to 00:00:38.039 --> 00:00:40.079 really explore the structure of the surprising 00:00:40.079 --> 00:00:43.140 history and the profound controversy surrounding 00:00:43.140 --> 00:00:45.880 these data aggregators. Our mission today is 00:00:45.880 --> 00:00:48.039 pretty straightforward. We are decoding the modern 00:00:48.039 --> 00:00:50.020 system that dictates your financial identity. 00:00:50.280 --> 00:00:52.219 We want to help you understand not just what 00:00:52.219 --> 00:00:54.380 your score is, but who is calculating it and 00:00:54.380 --> 00:00:57.200 why. And I think to start, we really need to 00:00:57.200 --> 00:00:59.520 clarify the language because the terminology 00:00:59.520 --> 00:01:01.520 actually changes quite a bit depending on where 00:01:01.520 --> 00:01:03.920 you are in the world. So the general term, the 00:01:03.920 --> 00:01:05.939 one most people probably recognize is the credit 00:01:05.939 --> 00:01:08.079 bureau. Right. That's the data collection agency. 00:01:08.219 --> 00:01:10.040 They're the ones gathering all that raw account 00:01:10.040 --> 00:01:12.500 information from your various creditors. But 00:01:12.500 --> 00:01:14.959 then once that raw data is collected, it has 00:01:14.959 --> 00:01:17.879 to go somewhere. It goes to an entity that processes 00:01:17.879 --> 00:01:20.780 it, packages it and then sells it. And that processor 00:01:20.780 --> 00:01:23.239 has different names globally. So in the United 00:01:23.239 --> 00:01:26.400 States. That organization is legally termed a 00:01:26.400 --> 00:01:29.719 Consumer Reporting Agency, or CRA for short. 00:01:29.879 --> 00:01:32.480 Okay, CRA in the U .S. But if you venture over 00:01:32.480 --> 00:01:34.599 to the U .K., you'll encounter a credit reference 00:01:34.599 --> 00:01:37.620 agency. Same job, different title. Exactly. Then 00:01:37.620 --> 00:01:39.700 you go to Australia, and it's a credit reporting 00:01:39.700 --> 00:01:43.019 body. And in major developing economies like 00:01:43.019 --> 00:01:46.439 India, they call them a credit information company, 00:01:46.519 --> 00:01:51.799 or CIC. So CRA, CIC, credit reporting body. Lots 00:01:51.799 --> 00:01:53.859 of names. But they all perform the same core 00:01:53.859 --> 00:01:56.760 function, collecting and aggregating your consumer 00:01:56.760 --> 00:01:59.799 data for assessment and for risk management. 00:01:59.980 --> 00:02:01.920 And crucially, I think we have to establish a 00:02:01.920 --> 00:02:04.260 foundational distinction right off the bat. This 00:02:04.260 --> 00:02:06.239 is a common confusion that our sources really 00:02:06.239 --> 00:02:08.979 stress we need to avoid. A credit bureau or a 00:02:08.979 --> 00:02:12.120 CRA is absolutely not the same as a credit rating 00:02:12.120 --> 00:02:15.539 agency. That is such an important point. They 00:02:15.539 --> 00:02:18.069 sound similar. But they operate in completely 00:02:18.069 --> 00:02:20.069 different worlds. Right. So when we talk about 00:02:20.069 --> 00:02:22.490 credit rating agencies, we're talking about Standard 00:02:22.490 --> 00:02:26.469 & Poor's, Moody's, those guys. Correct. And they 00:02:26.469 --> 00:02:29.030 are assessing the solvency and credit risk of 00:02:29.030 --> 00:02:32.370 huge entities, governments, massive corporations, 00:02:32.830 --> 00:02:35.750 complex financial products. Not people. Not individual 00:02:35.750 --> 00:02:38.990 people. We today are talking about the entities 00:02:38.990 --> 00:02:42.000 tracking your personal history. your utility 00:02:42.000 --> 00:02:44.340 payments, your mortgage, your credit card usage. 00:02:44.500 --> 00:02:47.520 It's all about individual microeconomic risk. 00:02:47.699 --> 00:02:50.719 So why is this intricate, often invisible system 00:02:50.719 --> 00:02:53.240 so important? I mean, why does it matter to you, 00:02:53.280 --> 00:02:55.439 the listener, that these agencies even exist? 00:02:55.800 --> 00:02:58.500 It matters because the entire system is built 00:02:58.500 --> 00:03:01.259 to solve a fundamental, almost ancient economic 00:03:01.259 --> 00:03:04.120 hurdle. And that hurdle is known as asymmetric 00:03:04.120 --> 00:03:06.719 information. Okay, let's unpack that term. Asymmetric 00:03:06.719 --> 00:03:09.659 information. It's a bit academic, but the concept 00:03:09.659 --> 00:03:12.060 is simple. It is. It just means that one party 00:03:12.060 --> 00:03:14.180 in a transaction knows more than the other party. 00:03:14.360 --> 00:03:18.080 Right. So in lending, you, the borrower, you 00:03:18.080 --> 00:03:20.360 know exactly how responsible you are, you know 00:03:20.360 --> 00:03:22.659 what your income truly is, and you know how willing 00:03:22.659 --> 00:03:25.080 you are to prioritize paying back a loan. All 00:03:25.080 --> 00:03:27.340 information the lender is completely in the dark 00:03:27.340 --> 00:03:30.319 about. Exactly. And when a lender operates in 00:03:30.319 --> 00:03:32.539 the dark, without knowing if you're a reliable 00:03:32.539 --> 00:03:35.120 payer or someone who might just, you know, disappear, 00:03:35.419 --> 00:03:39.069 they face... two massive potential pitfalls. 00:03:39.189 --> 00:03:42.090 And those are adverse selection and moral hazard. 00:03:42.250 --> 00:03:44.729 Two big ones. Let's define those clearly. So 00:03:44.729 --> 00:03:48.729 adverse selection. That's the problem where the 00:03:48.729 --> 00:03:51.330 people who are most eager to get a loan are precisely 00:03:51.330 --> 00:03:54.569 the ones who are the highest risk. The ones most 00:03:54.569 --> 00:03:56.590 likely to default. I always think of it like 00:03:56.590 --> 00:03:59.030 a medical insurance pool. If only the sickest 00:03:59.030 --> 00:04:01.349 people buy insurance, the whole system just collapses 00:04:01.349 --> 00:04:03.750 because the insurer can't possibly survive. That's 00:04:03.750 --> 00:04:06.430 a perfect analogy. Yeah. And if the bank manages 00:04:06.430 --> 00:04:09.389 to avoid that, they still face moral hazard. 00:04:09.590 --> 00:04:11.349 And that happens after the loan is already given 00:04:11.349 --> 00:04:14.490 out. Right. Once you have the money, your behavior 00:04:14.490 --> 00:04:16.829 might change. You know the bank is already on 00:04:16.829 --> 00:04:18.689 the hook, so maybe you become less disciplined. 00:04:19.209 --> 00:04:21.769 or you take on more risk, or you simply stop 00:04:21.769 --> 00:04:25.310 making that repayment your top priority. So the 00:04:25.310 --> 00:04:28.930 credit reporting system, it's like the antidote 00:04:28.930 --> 00:04:32.569 to this information failure. It is. By providing 00:04:32.569 --> 00:04:35.209 adequate, independent, and comprehensive credit 00:04:35.209 --> 00:04:38.529 information, the system helps alleviate both 00:04:38.529 --> 00:04:41.430 of those problems. How so? Well, it helps with 00:04:41.430 --> 00:04:43.370 adverse selection by allowing the bank to properly 00:04:43.370 --> 00:04:46.089 screen you before they lend. They can see your 00:04:46.089 --> 00:04:49.089 history. And it helps with moral hazard by letting 00:04:49.089 --> 00:04:51.949 the bank continuously monitor your activity across 00:04:51.949 --> 00:04:55.050 the entire financial ecosystem. And that screening 00:04:55.050 --> 00:04:57.329 and monitoring function is what makes the system 00:04:57.329 --> 00:04:59.889 so incredibly powerful in your life. Absolutely. 00:04:59.970 --> 00:05:02.589 The information they collect, package and score 00:05:02.589 --> 00:05:05.069 directly determines the interest rate you're 00:05:05.069 --> 00:05:06.930 going to be offered. Not just the interest rate, 00:05:06.949 --> 00:05:09.230 but the overall term length and really your ultimate 00:05:09.230 --> 00:05:11.370 access to all kinds of opportunities. And that 00:05:11.370 --> 00:05:13.709 interest rate isn't just some flat fee. It's 00:05:13.709 --> 00:05:15.670 a direct calculation of the risk you represent. 00:05:16.120 --> 00:05:18.220 all quantified by the credit bureau. So this 00:05:18.220 --> 00:05:20.660 deep dive is about understanding that immense 00:05:20.660 --> 00:05:24.060 power, how these institutions gather data, how 00:05:24.060 --> 00:05:26.680 they're regulated across the globe, and the profound 00:05:26.680 --> 00:05:29.759 and often secret formulas that shape our financial 00:05:29.759 --> 00:05:33.959 realities. All right, let's get into the mechanics 00:05:33.959 --> 00:05:36.379 of this. Before we can even talk about the score, 00:05:36.459 --> 00:05:38.779 we have to talk about the data itself. Where 00:05:38.779 --> 00:05:40.500 does all this personal financial information 00:05:40.500 --> 00:05:43.439 come from? What are the inputs into this vast 00:05:43.439 --> 00:05:47.110 system? The sources are, well, they're exhaustive. 00:05:47.949 --> 00:05:51.209 And they're referred to by a specific term, data 00:05:51.209 --> 00:05:53.730 furnishers. Data furnishers. And this is a term 00:05:53.730 --> 00:05:56.269 that really encompasses every entity that extends 00:05:56.269 --> 00:05:58.490 you credit or services and expects a regular 00:05:58.490 --> 00:06:00.529 payment. It's not just the big banks and credit 00:06:00.529 --> 00:06:02.329 card companies. So give us some specific examples. 00:06:02.529 --> 00:06:04.310 What's the full range of furnishers we're talking 00:06:04.310 --> 00:06:06.550 about? Okay, so you have the obvious ones. Mortgage 00:06:06.550 --> 00:06:09.610 lenders, auto finance companies, retailers who 00:06:09.610 --> 00:06:12.170 issue their own store cards. Sure. But crucially, 00:06:12.269 --> 00:06:14.769 the furnishers often include your utility companies, 00:06:15.050 --> 00:06:17.949 gas, electric, water, especially if you have 00:06:17.949 --> 00:06:20.490 a record of being late. They include your telecom 00:06:20.490 --> 00:06:23.310 providers, your cable company, and some newer 00:06:23.310 --> 00:06:26.079 models, even your landlord. So basically any 00:06:26.079 --> 00:06:29.279 interaction where a debt or an ongoing obligation 00:06:29.279 --> 00:06:31.959 is created. That's the idea. They're trying to 00:06:31.959 --> 00:06:33.800 collect data on every single one. And it goes 00:06:33.800 --> 00:06:36.480 beyond just the active current accounts you have. 00:06:36.579 --> 00:06:39.060 It also includes the entities that deal with 00:06:39.060 --> 00:06:41.339 the fallout when you don't pay. Oh, absolutely. 00:06:41.839 --> 00:06:44.720 Debt collection agencies are key data furnishers. 00:06:44.759 --> 00:06:47.480 If your account gets charged off or sold to a 00:06:47.480 --> 00:06:50.019 collector, that information gets sent right to 00:06:50.019 --> 00:06:52.459 the bureau. And what about the legal system? 00:06:52.720 --> 00:06:55.399 Critically, the bureaus also draw from public 00:06:55.399 --> 00:06:57.959 records provided by the courts. So this includes 00:06:57.959 --> 00:07:01.240 significant court adjudicated debt obligations. 00:07:01.279 --> 00:07:04.480 Things like tax liens or civil judgments. Or 00:07:04.480 --> 00:07:06.000 bankruptcies. All of that becomes part of your 00:07:06.000 --> 00:07:08.800 file. That seems like an unbelievably enormous 00:07:08.800 --> 00:07:12.720 net that they cast. It is. And all this disparate 00:07:12.720 --> 00:07:15.480 data from your electric bill to a public court 00:07:15.480 --> 00:07:18.740 record, it's all aggregated into the CRA's massive 00:07:18.740 --> 00:07:21.430 repository. And the sole reason for gathering 00:07:21.430 --> 00:07:23.709 this colossal amount of data is, at its heart, 00:07:23.769 --> 00:07:26.550 to evaluate your creditworthiness. Which is defined 00:07:26.550 --> 00:07:28.949 pretty simply. It's just your demonstrated ability 00:07:28.949 --> 00:07:31.889 and willingness to pay back a loan. The assessment 00:07:31.889 --> 00:07:34.430 is cold, it's numerical, and it's based purely 00:07:34.430 --> 00:07:36.269 on your historical patterns. And this is the 00:07:36.269 --> 00:07:38.970 engine that drives a really crucial concept in 00:07:38.970 --> 00:07:43.089 modern finance. Risk -based pricing. Yes. Okay, 00:07:43.149 --> 00:07:45.610 risk -based pricing. We mentioned it briefly, 00:07:45.769 --> 00:07:47.910 but let's slow down and really dig into that. 00:07:48.089 --> 00:07:50.470 It's a very technical way of saying price discrimination. 00:07:51.560 --> 00:07:54.339 How does the data translate into that practice? 00:07:54.680 --> 00:07:57.180 It translates directly into the cost of money 00:07:57.180 --> 00:08:01.040 for you. The CRAs provide data that lets lenders 00:08:01.040 --> 00:08:04.079 categorize all of us, all borrowers, into different 00:08:04.079 --> 00:08:07.439 risk buckets. So if I, the consumer, have a history 00:08:07.439 --> 00:08:10.439 of poor credit repayment, or maybe I have high 00:08:10.439 --> 00:08:12.439 utilization of my available credit. Or those 00:08:12.439 --> 00:08:14.439 court -adjudicated debts we mentioned, like a 00:08:14.439 --> 00:08:16.399 bankruptcy. Then the lender sees me as a higher 00:08:16.399 --> 00:08:19.079 risk. Exactly. And what's the cost of being put 00:08:19.079 --> 00:08:21.000 in that higher risk bucket? You pay a high... 00:08:21.000 --> 00:08:23.259 annual interest rate. Right. And it's not just 00:08:23.259 --> 00:08:25.720 about charging you more because they can. The 00:08:25.720 --> 00:08:28.540 lender is using that CRA data to calculate the 00:08:28.540 --> 00:08:31.139 expected default rate of your entire risk profile. 00:08:31.379 --> 00:08:34.059 So let's say they expect 5 % of borrowers in 00:08:34.059 --> 00:08:36.759 my bucket to default on their loans. Then they 00:08:36.759 --> 00:08:38.419 need to charge high enough interest to the other 00:08:38.419 --> 00:08:41.779 95 % of you to cover those losses and still make 00:08:41.779 --> 00:08:44.980 their profit. So the system, in effect, works 00:08:44.980 --> 00:08:48.440 by monetizing past mistakes. It forces those 00:08:48.440 --> 00:08:51.240 with poor histories to essentially subsidize 00:08:51.240 --> 00:08:53.100 the risk they present to the whole financial 00:08:53.100 --> 00:08:56.879 system. Precisely. And conversely, the lower 00:08:56.879 --> 00:08:59.159 your perceived risk, which means a high score, 00:08:59.399 --> 00:09:01.460 the lower the price of the money you borrow. 00:09:01.960 --> 00:09:05.240 That can save you thousands, even tens of thousands 00:09:05.240 --> 00:09:07.259 of dollars over the life of a single mortgage 00:09:07.259 --> 00:09:10.379 or auto loan. And all of this is only possible 00:09:10.379 --> 00:09:12.860 because modern economies deal with what our sources 00:09:12.860 --> 00:09:15.740 call a large number of consumer borrowers. I 00:09:15.740 --> 00:09:17.879 mean, it's an almost infinite number. Manual 00:09:17.879 --> 00:09:19.899 risk assessment for every single person is completely 00:09:19.899 --> 00:09:22.019 impossible. Which means the resulting system 00:09:22.019 --> 00:09:25.240 has to be mechanistic. It must be. The machine 00:09:25.240 --> 00:09:28.240 takes that massive file of data and applies a 00:09:28.240 --> 00:09:31.139 proprietary mathematical algorithm to generate 00:09:31.139 --> 00:09:33.340 that single essential number, your credit score. 00:09:33.539 --> 00:09:36.690 And that score is the shortcut. It's the metric 00:09:36.690 --> 00:09:39.590 a lender uses to just rapidly assess your risk 00:09:39.590 --> 00:09:42.090 of default. It's incredibly efficient. It boils 00:09:42.090 --> 00:09:45.730 down decades of your financial history to a single 00:09:45.730 --> 00:09:50.070 number, usually between, what, 300 and 850 in 00:09:50.070 --> 00:09:52.389 the US system. The key takeaway there is efficiency 00:09:52.389 --> 00:09:55.409 and standardization. A lender in California can 00:09:55.409 --> 00:09:57.889 instantly apply the same risk standards to a 00:09:57.889 --> 00:10:00.070 million different people all over the country 00:10:00.070 --> 00:10:02.669 without ever meeting a single one of them. Now, 00:10:02.690 --> 00:10:04.889 what's really fascinating here is that the predictive 00:10:04.889 --> 00:10:07.590 power of these credit records has started extending 00:10:07.590 --> 00:10:09.970 far beyond the realm of traditional lending. 00:10:10.129 --> 00:10:11.870 Yeah, this is where it gets really interesting 00:10:11.870 --> 00:10:14.389 and maybe a little unsettling. Decision makers 00:10:14.389 --> 00:10:17.590 in areas entirely unrelated to loans are now 00:10:17.590 --> 00:10:20.639 relying on credit records because. Well, studies 00:10:20.639 --> 00:10:22.500 have shown they possess this surprising predictive 00:10:22.500 --> 00:10:24.940 value. And the two big surprising applications 00:10:24.940 --> 00:10:27.019 we need to highlight are employment screening 00:10:27.019 --> 00:10:29.279 and the underwriting of property and casualty 00:10:29.279 --> 00:10:31.240 insurance. Let's think about insurance first. 00:10:31.659 --> 00:10:34.460 An insurer uses a variation of your credit history, 00:10:34.559 --> 00:10:36.980 they often call it an insurance score, to underwrite 00:10:36.980 --> 00:10:39.220 your risk. So how does my credit card payment 00:10:39.220 --> 00:10:42.120 history predict if I'm going to, say, crash my 00:10:42.120 --> 00:10:44.379 car? Well, the studies have indicated that a 00:10:44.379 --> 00:10:47.840 consumer who is financially careless maybe someone 00:10:47.840 --> 00:10:50.240 who is chronically missing credit card payments, 00:10:50.480 --> 00:10:54.440 is statistically more likely to file property 00:10:54.440 --> 00:10:56.679 claims. Regardless of whether those claims are 00:10:56.679 --> 00:10:58.759 fraud or not. Right. The data just shows a correlation. 00:10:59.259 --> 00:11:02.000 So your history of paying your bills on time 00:11:02.000 --> 00:11:04.639 now directly informs the price of your homeowners 00:11:04.639 --> 00:11:07.159 or your auto insurance premium. And then there's 00:11:07.159 --> 00:11:09.759 employment screening, which is maybe the most 00:11:09.759 --> 00:11:11.919 controversial application outside of lending. 00:11:12.320 --> 00:11:14.720 Why would an employer care if I missed a payment 00:11:14.720 --> 00:11:16.700 three years ago? It's the same predictive logic. 00:11:17.129 --> 00:11:19.370 Employers will argue that an individual's financial 00:11:19.370 --> 00:11:21.769 responsibility reflects their overall sense of 00:11:21.769 --> 00:11:23.470 responsibility. So they're trying to predict 00:11:23.470 --> 00:11:26.570 things like employee turnover, maybe theft or 00:11:26.570 --> 00:11:29.370 just general reliability on the job. Exactly. 00:11:29.879 --> 00:11:32.299 Now, many jurisdictions heavily regulate this 00:11:32.299 --> 00:11:35.019 practice, but it definitely still happens. And 00:11:35.019 --> 00:11:37.039 it means your private payment behavior is now 00:11:37.039 --> 00:11:38.860 influencing your professional opportunities. 00:11:39.220 --> 00:11:42.100 So while the system is primarily designed to 00:11:42.100 --> 00:11:45.039 help lenders avoid risk, the sources do also 00:11:45.039 --> 00:11:47.419 highlight some systemic benefits for the consumer. 00:11:48.179 --> 00:11:51.269 Assuming the system is robust and fair. Absolutely. 00:11:51.470 --> 00:11:54.570 A good, functional credit information system, 00:11:54.669 --> 00:11:57.769 it reduces the effect of a credit monopoly from 00:11:57.769 --> 00:12:00.710 large, powerful banks. How does it do that? Well, 00:12:00.870 --> 00:12:03.909 if your credit history is portable and transparent, 00:12:04.269 --> 00:12:08.110 a smaller regional bank or a credit union can 00:12:08.110 --> 00:12:10.389 confidently assess your risk and then compete 00:12:10.389 --> 00:12:12.889 for your business. Which drives down the overall 00:12:12.889 --> 00:12:14.870 cost of borrowing through simple competition. 00:12:15.250 --> 00:12:18.080 And fundamentally... It provides very clear, 00:12:18.200 --> 00:12:20.740 immediate, and high -stakes incentives for all 00:12:20.740 --> 00:12:24.080 of us as borrowers to repay on time. Good behavior 00:12:24.080 --> 00:12:26.600 is rewarded with better access and lower prices. 00:12:26.820 --> 00:12:29.120 Which is why the advice from consumer welfare 00:12:29.120 --> 00:12:32.159 advocates is so consistent and so crucial. You 00:12:32.159 --> 00:12:34.039 have to be an active participant. Right. You 00:12:34.039 --> 00:12:36.620 must monitor your own financial identity. They 00:12:36.620 --> 00:12:38.519 advise you to review your reports at least once 00:12:38.519 --> 00:12:40.759 a year to ensure accuracy, because if you don't 00:12:40.759 --> 00:12:42.779 check, the risk of a simple administrative error 00:12:42.779 --> 00:12:44.840 costing you dearly just goes up exponentially. 00:12:45.299 --> 00:12:47.360 And finally, let's just note the expanding role 00:12:47.360 --> 00:12:50.000 of these agencies. They're now becoming authoritative 00:12:50.000 --> 00:12:52.779 sources of identity information. They're moving 00:12:52.779 --> 00:12:55.960 from just assessing your debt to verifying who 00:12:55.960 --> 00:12:58.360 you are. They've become these linchpins in our 00:12:58.360 --> 00:13:00.600 digital security architecture. Yeah, CRAs are 00:13:00.600 --> 00:13:02.580 frequently used for identity verification services 00:13:02.580 --> 00:13:04.899 and for something called knowledge -based authentication, 00:13:05.200 --> 00:13:08.179 or KBA. That's when you try to access a secure 00:13:08.179 --> 00:13:11.100 online account and the system asks a question 00:13:11.100 --> 00:13:13.539 that only you should know the answer to. Exactly. 00:13:13.539 --> 00:13:16.480 Like which of these monthly payment amounts matches 00:13:16.480 --> 00:13:19.580 a loan you took out in 2018? And that KBA data 00:13:19.580 --> 00:13:22.000 is pulled directly from their deep historical 00:13:22.000 --> 00:13:24.940 files on you. So the CRA's role has expanded 00:13:24.940 --> 00:13:29.500 from merely assessing risk in finance to underpinning 00:13:29.500 --> 00:13:31.860 security and verification across the entire digital 00:13:31.860 --> 00:13:33.919 ecosystem. It's a huge amount of centralized 00:13:33.919 --> 00:13:37.039 private power. Now we get to switch gears and 00:13:37.039 --> 00:13:40.360 travel back in time a bit. The modern system, 00:13:40.419 --> 00:13:43.500 all digitized, algorithm driven, it feels like 00:13:43.500 --> 00:13:45.679 a product of the late 20th century. It does. 00:13:45.960 --> 00:13:48.279 But the origins of organized credit surveillance 00:13:48.279 --> 00:13:51.480 go much, much deeper. They really do. It's a 00:13:51.480 --> 00:13:55.059 remarkable journey from private whisper networks 00:13:55.059 --> 00:13:58.960 to these global databases. We can trace the origins 00:13:58.960 --> 00:14:01.860 of commercial credit reporting in the U .S. all 00:14:01.860 --> 00:14:04.539 the way back to a major financial cataclysm. 00:14:04.879 --> 00:14:08.799 The Panic of 1837. A catastrophic period. Bank 00:14:08.799 --> 00:14:12.379 failures, economic depression. Total chaos. And 00:14:12.379 --> 00:14:14.639 when that kind of trust vanishes from an economy, 00:14:14.799 --> 00:14:17.179 people need a structured way to assess who they 00:14:17.179 --> 00:14:20.399 can safely lend to or trade with. So necessity 00:14:20.399 --> 00:14:23.440 is the mother of invention. Pretty much. The 00:14:23.440 --> 00:14:26.259 need for reliable, independent commercial information 00:14:26.259 --> 00:14:28.679 sparked the formation of the first commercial 00:14:28.679 --> 00:14:31.379 credit reporting organizations. And they were 00:14:31.379 --> 00:14:33.580 designed initially for wholesalers and large 00:14:33.580 --> 00:14:35.940 merchants dealing with other businesses, not 00:14:35.940 --> 00:14:38.379 individuals. And these early formats were completely 00:14:38.379 --> 00:14:40.620 analog. I mean, we're talking about coded reference 00:14:40.620 --> 00:14:43.360 books by the 1850s. I'm picturing something heavy, 00:14:43.519 --> 00:14:45.740 leather bound, full of inscrutable symbols. And 00:14:45.740 --> 00:14:47.440 that's precisely right. There were physical ledgers 00:14:47.440 --> 00:14:50.559 or books only available to subscribing wholesalers, 00:14:50.600 --> 00:14:53.039 banks and insurance companies. up a merchant 00:14:53.039 --> 00:14:56.299 or a company and their credit reputation wasn't 00:14:56.299 --> 00:14:58.919 written out plainly it was rendered in a special 00:14:58.919 --> 00:15:01.779 code that only the subscribers understood it 00:15:01.779 --> 00:15:04.480 was like the proprietary language of trust what 00:15:04.480 --> 00:15:07.299 kind of information did these coded ledgers even 00:15:07.299 --> 00:15:10.779 hold was it just good or bad No, it was often 00:15:10.779 --> 00:15:14.259 highly nuanced and incredibly subjective. While 00:15:14.259 --> 00:15:16.899 they tracked financial facts, like whether a 00:15:16.899 --> 00:15:19.000 business failed to pay an invoice, they also 00:15:19.000 --> 00:15:21.279 contained assessments of character, reputation 00:15:21.279 --> 00:15:24.919 in the community, and even moral habits. It was 00:15:24.919 --> 00:15:27.100 reputation tracking as much as it was financial 00:15:27.100 --> 00:15:30.139 tracking. The system was slow, but it provided 00:15:30.139 --> 00:15:32.830 some measure of due diligence. The first major 00:15:32.830 --> 00:15:37.029 technological shift then comes in 1875. This 00:15:37.029 --> 00:15:39.389 is when handwritten ledgers, which were so cumbersome 00:15:39.389 --> 00:15:41.470 to replicate an update, were replaced by the 00:15:41.470 --> 00:15:44.009 use of typed reports. A small step, but a step 00:15:44.009 --> 00:15:46.149 toward formalizing the whole process. And as 00:15:46.149 --> 00:15:48.549 the U .S. economy grew and urbanized in the late 00:15:48.549 --> 00:15:51.129 19th century, that surveillance net starts to 00:15:51.129 --> 00:15:53.330 drop from tracking businesses to tracking the 00:15:53.330 --> 00:15:57.350 individual customer. Yes. By the end of the 1880s, 00:15:57.350 --> 00:15:59.370 the systematic surveillance of retail customers 00:15:59.370 --> 00:16:01.669 was firmly established in major urban centers. 00:16:01.850 --> 00:16:04.049 People were starting to buy things on credit, 00:16:04.129 --> 00:16:07.629 and retailers needed accountability. This moves 00:16:07.629 --> 00:16:10.090 us into the late 1890s, where credit management 00:16:10.090 --> 00:16:13.629 starts to professionalize. It's moving beyond 00:16:13.629 --> 00:16:16.129 just the local shopkeeper knowing who's good 00:16:16.129 --> 00:16:19.100 for it. Exactly. Credit management professionalized 00:16:19.100 --> 00:16:21.639 and it became systematized. Credit worthiness 00:16:21.639 --> 00:16:24.179 was no longer just a hunch. It was increasingly 00:16:24.179 --> 00:16:27.179 categorized. These new professionals developed 00:16:27.179 --> 00:16:30.179 definitions for what constituted good, fair and 00:16:30.179 --> 00:16:33.559 poor credit risks. And that systematization was 00:16:33.559 --> 00:16:36.720 essential as mass retail really took hold. And 00:16:36.720 --> 00:16:39.000 then came the credit explosion of the early 20th 00:16:39.000 --> 00:16:42.250 century. The 1910s and 1920s, the roaring 20s, 00:16:42.269 --> 00:16:44.090 were essentially financed by readily available 00:16:44.090 --> 00:16:46.350 consumer credit. So our sources note that credit 00:16:46.350 --> 00:16:48.610 managers weren't just passively documenting payments 00:16:48.610 --> 00:16:50.690 anymore. They were becoming pretty sophisticated 00:16:50.690 --> 00:16:53.370 marketers. Absolutely. They started mining customer 00:16:53.370 --> 00:16:56.389 information for targeted sales promotions. They 00:16:56.389 --> 00:16:58.370 recognized that the data they collected had value 00:16:58.370 --> 00:17:01.009 way beyond just risk assessment. It could actively 00:17:01.009 --> 00:17:03.590 drive revenue. The scale of it was just astonishing 00:17:03.590 --> 00:17:06.650 for the time. By the early 20th century, you 00:17:06.650 --> 00:17:09.640 have stores interviewing, documenting. and tracking 00:17:09.640 --> 00:17:12.099 customers in something like 35 ,000 different 00:17:12.099 --> 00:17:14.940 credit departments across the U .S. It's decentralized, 00:17:15.079 --> 00:17:17.859 but it's massive surveillance by any standard. 00:17:18.039 --> 00:17:20.559 And this expansion, it naturally led to friction. 00:17:20.740 --> 00:17:23.220 National industries, automobile manufacturers, 00:17:23.700 --> 00:17:26.559 petroleum companies, direct sales, they needed 00:17:26.559 --> 00:17:28.960 consistent standards. Right. They couldn't rely 00:17:28.960 --> 00:17:31.160 on a thousand different local credit bureaus, 00:17:31.160 --> 00:17:33.460 each with its own methodology and its own pricing. 00:17:33.869 --> 00:17:36.349 And this desire for standardization and control 00:17:36.349 --> 00:17:39.109 became so intense that an antitrust petition 00:17:39.109 --> 00:17:42.089 was filed in 1933. Yes, the industry's consolidation 00:17:42.089 --> 00:17:44.630 trend was really born out of a perceived necessity 00:17:44.630 --> 00:17:47.569 for consistency. And that pressure for efficiency 00:17:47.569 --> 00:17:50.690 just kept mounting. The old credit rating books, 00:17:50.869 --> 00:17:53.890 they became obsolete as flexible card files and 00:17:53.890 --> 00:17:56.190 the widespread use of the telephone took over. 00:17:56.329 --> 00:17:58.990 The speed of information was everything. The 00:17:58.990 --> 00:18:01.710 need for speed is such a key historical driver 00:18:01.710 --> 00:18:04.089 here. We have a specific... detail that illustrates 00:18:04.089 --> 00:18:06.910 this perfectly. A teletype report, which was 00:18:06.910 --> 00:18:09.410 revolutionary technology for the time, took only 00:18:09.410 --> 00:18:12.470 one minute to send. That's compared to five minutes 00:18:12.470 --> 00:18:14.329 to communicate the same information reliably 00:18:14.329 --> 00:18:17.009 over the telephone. So that's a five -fold increase 00:18:17.009 --> 00:18:19.690 in efficiency? Exactly. And that was the competitive 00:18:19.690 --> 00:18:23.009 edge. But the true pivotal shift into the modern 00:18:23.009 --> 00:18:25.710 era we know today, that was computerization. 00:18:26.069 --> 00:18:29.529 Yes. In 1965, the first computerized credit bureau 00:18:29.529 --> 00:18:33.039 went online. And this... This marks the moment 00:18:33.039 --> 00:18:35.359 when the industry transitioned from being a cumbersome 00:18:35.359 --> 00:18:38.339 physical filing system to a scalable digital 00:18:38.339 --> 00:18:40.660 database. Everything changes then. Everything. 00:18:41.279 --> 00:18:44.000 Automation meant that data could be linked, stored, 00:18:44.200 --> 00:18:47.839 and retrieved with unimaginable speed. And that 00:18:47.839 --> 00:18:50.059 launched the consolidation trend we see today 00:18:50.059 --> 00:18:53.039 with the big three. However, those early automated 00:18:53.039 --> 00:19:04.730 reports were... This is a critical point that 00:19:04.730 --> 00:19:06.730 shows the early lack of boundaries. They weren't 00:19:06.730 --> 00:19:08.390 just looking at your number of missed payments. 00:19:08.609 --> 00:19:10.349 They might inquire about whether you were deemed 00:19:10.349 --> 00:19:13.369 temperamental, or if you were seen drinking too 00:19:13.369 --> 00:19:16.009 much at the local tavern, or if you had a chronic 00:19:16.009 --> 00:19:19.089 medical condition. These subjective character 00:19:19.089 --> 00:19:21.450 judgments were baked right into your financial 00:19:21.450 --> 00:19:24.569 viability score. And this invasive nature is 00:19:24.569 --> 00:19:27.250 what really alarmed lawmakers in the lead up 00:19:27.250 --> 00:19:30.269 to the landmark 1970 Fair Credit Reporting Act, 00:19:30.369 --> 00:19:33.450 or FCRA. The hearings for that act revealed that 00:19:33.450 --> 00:19:36.990 individuals felt Utterly helpless. If a subjective 00:19:36.990 --> 00:19:39.289 error was filed about their personality or their 00:19:39.289 --> 00:19:41.329 health information they couldn't even see, they 00:19:41.329 --> 00:19:43.690 had absolutely no way to clear it up or dispute 00:19:43.690 --> 00:19:45.890 the claim. So the legislative consensus was that 00:19:45.890 --> 00:19:48.029 the industry had become too powerful and way 00:19:48.029 --> 00:19:50.490 too opaque. And that it necessitated federal 00:19:50.490 --> 00:19:52.750 intervention to grant consumers basic rights 00:19:52.750 --> 00:19:55.619 of access and correction. So as the industry 00:19:55.619 --> 00:19:58.420 rapidly moved towards standardization, computerization, 00:19:58.640 --> 00:20:00.400 and the wide adoption of credit scoring in the 00:20:00.400 --> 00:20:03.180 1960s, the U .S. government began that slow, 00:20:03.380 --> 00:20:06.039 necessary process of implementing the rules of 00:20:06.039 --> 00:20:08.960 engagement for this new, powerful form of consumer 00:20:08.960 --> 00:20:11.680 surveillance. That history of centralization 00:20:11.680 --> 00:20:14.740 and then necessary regulation brings us to the 00:20:14.740 --> 00:20:17.670 modern era. The system that evolved in the U 00:20:17.670 --> 00:20:20.230 .S. is largely private and market driven, but 00:20:20.230 --> 00:20:23.049 that is far from the universal model. Let's start 00:20:23.049 --> 00:20:24.869 with that U .S. regulatory framework. Right. 00:20:24.930 --> 00:20:27.150 In the U .S., as we established, the legal term 00:20:27.150 --> 00:20:30.710 is Consumer Reporting Agency, or CRA. The framework 00:20:30.710 --> 00:20:33.190 is defined by a pretty dense federal regulatory 00:20:33.190 --> 00:20:35.980 environment, which... Honestly, it often confuses 00:20:35.980 --> 00:20:37.660 people because of the overlapping nature of all 00:20:37.660 --> 00:20:39.680 the laws. Yeah. Let's break down that alphabet 00:20:39.680 --> 00:20:41.819 soup of regulation. We have the Foundational 00:20:41.819 --> 00:20:44.740 Fair Credit Reporting Act, FCRA, passed in 1970. 00:20:45.380 --> 00:20:47.660 What did that fundamentally establish for people? 00:20:48.180 --> 00:20:51.660 FCRA gave you, the consumer, the right to know 00:20:51.660 --> 00:20:53.779 what is in your report. It gave you the right 00:20:53.779 --> 00:20:56.519 to dispute inaccurate information. And it placed 00:20:56.519 --> 00:20:59.500 limits on who can access your information and 00:20:59.500 --> 00:21:02.539 for how long. It's really the cornerstone of 00:21:02.539 --> 00:21:04.960 consumer protection in this space. Then in the 00:21:04.960 --> 00:21:07.460 2000s, it was significantly strengthened by the 00:21:07.460 --> 00:21:09.980 Fair and Accurate Credit Transactions Act, or 00:21:09.980 --> 00:21:12.519 FACTA. What was the most important thing FACTA 00:21:12.519 --> 00:21:15.680 added? Well, FACTA significantly enhanced consumer 00:21:15.680 --> 00:21:18.619 rights. It's the law that mandated free annual 00:21:18.619 --> 00:21:20.579 reports from the CRAs, which we should definitely 00:21:20.579 --> 00:21:23.569 talk about. But perhaps more crucially, it mandated 00:21:23.569 --> 00:21:26.069 stricter requirements for addressing identity 00:21:26.069 --> 00:21:29.049 theft. And it placed specific duties on the data 00:21:29.049 --> 00:21:31.529 furnishers, the banks, to actually investigate 00:21:31.529 --> 00:21:34.730 disputes. Before FACTA, furnishers often just 00:21:34.730 --> 00:21:37.170 dismissed disputes very quickly. So ACTA compelled 00:21:37.170 --> 00:21:39.670 them to actually participate in the dispute resolution 00:21:39.670 --> 00:21:42.269 process. Yes, it put the onus on them, too. We 00:21:42.269 --> 00:21:44.930 also have the Fair Credit Billing Act, FCBA, 00:21:45.170 --> 00:21:47.750 and Regulation B. And those primarily deal with 00:21:47.750 --> 00:21:50.089 credit card disputes and anti -discrimination. 00:21:50.830 --> 00:21:53.450 ensuring equal access to credit regardless of 00:21:53.450 --> 00:21:56.230 protected characteristics like race or gender. 00:21:56.450 --> 00:21:58.349 Now let's look at the U .S. oversight structure 00:21:58.349 --> 00:22:01.009 because it's split. And that split is based on 00:22:01.009 --> 00:22:03.390 functional expertise. It is. The Federal Trade 00:22:03.390 --> 00:22:06.750 Commission, the FTC, holds the primary enforcement 00:22:06.750 --> 00:22:09.569 and regulatory oversight for the CRAs themselves. 00:22:10.049 --> 00:22:12.589 They regulate the agencies creating the product. 00:22:12.690 --> 00:22:15.089 But the CRAs rely entirely on the data coming 00:22:15.089 --> 00:22:17.289 from the banks and lenders. That's where the 00:22:17.289 --> 00:22:19.470 other regulator comes in. The Office of the Controller 00:22:19.470 --> 00:22:24.230 of the Currency, or OCC. Right. The OCC. regulates 00:22:24.230 --> 00:22:27.869 and supervises national banks, those large federally 00:22:27.869 --> 00:22:30.369 chartered institutions, specifically regarding 00:22:30.369 --> 00:22:33.089 the quality, the completeness, and the tineliness 00:22:33.089 --> 00:22:35.509 of the data they furnish to the CRAs. So the 00:22:35.509 --> 00:22:38.450 FTC regulates the final product, and the OCC 00:22:38.450 --> 00:22:40.910 regulates the quality of the raw materials coming 00:22:40.910 --> 00:22:42.970 from the biggest financial institutions. It's 00:22:42.970 --> 00:22:45.150 a checks and balances system designed to maintain 00:22:45.150 --> 00:22:47.369 the integrity of the information supply chain. 00:22:47.569 --> 00:22:50.539 And who are the actors in this market? The sources 00:22:50.539 --> 00:22:53.500 identify the four national traditional CRAs in 00:22:53.500 --> 00:22:57.200 the U .S. We have Experian, Equifax, TransUnion, 00:22:57.200 --> 00:22:59.539 and Inovus. The big three are household names. 00:23:00.039 --> 00:23:02.700 Inovus is a bit smaller. But what unites them 00:23:02.700 --> 00:23:05.559 is that they're all entirely for -profit businesses 00:23:05.559 --> 00:23:08.539 and have zero direct government affiliation. 00:23:08.799 --> 00:23:11.880 This is a private industry, collecting and selling 00:23:11.880 --> 00:23:14.680 consumer data for profit. And yet... They have 00:23:14.680 --> 00:23:16.980 to standardize their operations to function efficiently. 00:23:17.279 --> 00:23:19.539 They're all members of the Consumer Data Industry 00:23:19.539 --> 00:23:23.460 Association, CDIA. Right. The CDIA is the industry's 00:23:23.460 --> 00:23:25.859 voice. It establishes the technical reporting 00:23:25.859 --> 00:23:28.400 standards. The current standard that's accepted 00:23:28.400 --> 00:23:31.700 by all the major U .S. CRAs is called Metro 2. 00:23:32.089 --> 00:23:34.390 And that standardization is what allows a bank 00:23:34.390 --> 00:23:37.509 to report data to all four CRAs using the exact 00:23:37.509 --> 00:23:39.869 same format. Without Metro 2, the whole system 00:23:39.869 --> 00:23:41.809 would just descend into chaos and incompatibility. 00:23:42.029 --> 00:23:44.349 That standardization is also key to the consumer 00:23:44.349 --> 00:23:46.869 benefit afforded by FACTA, the right to a free 00:23:46.869 --> 00:23:50.130 annual credit report from three nationwide CRAs 00:23:50.130 --> 00:23:52.829 accessed via one central portal, annualcreditreport 00:23:52.829 --> 00:23:55.609 .com. And we also see market -driven attempts 00:23:55.609 --> 00:23:57.789 to address the gaps in that traditional model, 00:23:57.930 --> 00:24:00.390 which often ignores nontraditional payments. 00:24:00.859 --> 00:24:03.779 This brings up models like PRBC, which stands 00:24:03.779 --> 00:24:06.960 for Payment Reporting Builds Credit, Inc. PRBC 00:24:06.960 --> 00:24:08.859 is fascinating because it tackles the problem 00:24:08.859 --> 00:24:11.559 of the credit invisible. Right. People who pay 00:24:11.559 --> 00:24:13.799 their rent and utility bills on time every month, 00:24:13.859 --> 00:24:16.099 but don't have enough credit card history to 00:24:16.099 --> 00:24:19.259 even get a score. So it allows consumers to self 00:24:19.259 --> 00:24:21.819 -enroll and actually report these on -time payments, 00:24:22.000 --> 00:24:24.720 creating a positive credit file where none existed 00:24:24.720 --> 00:24:26.900 before. It's a market -based solution trying 00:24:26.900 --> 00:24:29.099 to provide a wider scope of consumer behavior. 00:24:29.759 --> 00:24:31.400 Now let's shift across the Atlantic to the United 00:24:31.400 --> 00:24:34.180 Kingdom. Here, the core agencies are the same 00:24:34.180 --> 00:24:37.160 names, Experian, Equifax, and TransUnion. But 00:24:37.160 --> 00:24:40.779 the regulatory foundation is, well, it's distinctly 00:24:40.779 --> 00:24:42.599 more public interest driven, especially when 00:24:42.599 --> 00:24:44.420 it comes to privacy. They're governed by the 00:24:44.420 --> 00:24:47.140 Consumer Credit Act 1974 and the more recent 00:24:47.140 --> 00:24:50.779 Data Protection Act 2018. And that DPA 2018 is 00:24:50.779 --> 00:24:53.339 crucial. It takes a very strong stance on personal 00:24:53.339 --> 00:24:56.680 data. It mandates that data held by CRAs must 00:24:56.680 --> 00:24:59.200 be accurate, relevant, held for a proper purpose, 00:24:59.319 --> 00:25:02.539 and cannot be out of date. And that strong framework 00:25:02.539 --> 00:25:05.460 gives individuals a massive advantage over the 00:25:05.460 --> 00:25:07.579 U .S. system. It really does. It's the legal 00:25:07.579 --> 00:25:09.720 right to access their consumer credit report 00:25:09.720 --> 00:25:12.460 upon request, known as a statutory credit report, 00:25:12.759 --> 00:25:16.720 for a minimal fee or often for free. It's a mandate 00:25:16.720 --> 00:25:19.400 that strengthens the consumer's foothold in a 00:25:19.400 --> 00:25:23.000 system that often feels so abstract. Now, moving 00:25:23.000 --> 00:25:25.000 beyond the North American and Western European 00:25:25.000 --> 00:25:27.900 models, we see even greater structural differences. 00:25:28.339 --> 00:25:31.039 These are often driven by direct government response 00:25:31.039 --> 00:25:34.420 to national macroeconomic crises. This brings 00:25:34.420 --> 00:25:36.799 us to public and state supported systems in places 00:25:36.799 --> 00:25:39.039 like Asia and the Middle East. Let's start with 00:25:39.039 --> 00:25:41.599 India. OK. The country's first credit information 00:25:41.599 --> 00:25:44.859 bureau was Sybil. which is now promoted by TransUnion. 00:25:45.059 --> 00:25:47.299 And it wasn't born out of a private market need. 00:25:47.440 --> 00:25:49.579 It came out of a necessity that was mandated 00:25:49.579 --> 00:25:52.720 by the Reserve Bank of India, the RBI. So why 00:25:52.720 --> 00:25:55.059 did the RBI, the central bank, have to step in 00:25:55.059 --> 00:25:57.380 and mandate this? Because of a massive problem 00:25:57.380 --> 00:25:59.940 they had with non -performing assets or NPAs. 00:26:00.349 --> 00:26:02.549 So loans were the borrower was failing to make 00:26:02.549 --> 00:26:05.750 payments. And it was destabilizing the entire 00:26:05.750 --> 00:26:09.890 banking sector. The RBI saw a structured, centralized 00:26:09.890 --> 00:26:12.970 credit bureau as an essential tool to contain 00:26:12.970 --> 00:26:16.109 these NPAs, to improve lending discipline, and 00:26:16.109 --> 00:26:18.170 ultimately to stabilize the national financial 00:26:18.170 --> 00:26:20.869 system. So it wasn't about consumer benefit first. 00:26:20.910 --> 00:26:23.349 It was about systemic financial health. It was 00:26:23.349 --> 00:26:26.250 a tool of macroeconomic policy, first and foremost. 00:26:26.829 --> 00:26:29.789 And India has since approved other bureaus, including 00:26:29.789 --> 00:26:32.849 CRI Highmark, which showcases a completely different 00:26:32.849 --> 00:26:35.349 segment of the market. CRI Highmark is globally 00:26:35.349 --> 00:26:37.630 significant because it operates the world's largest 00:26:37.630 --> 00:26:40.410 microfinance bureau. It serves hundreds of millions 00:26:40.410 --> 00:26:43.089 of people who borrow very small amounts, often 00:26:43.089 --> 00:26:45.769 for agriculture or rural enterprise or tiny businesses. 00:26:46.069 --> 00:26:48.210 This shows how a credit reporting system can 00:26:48.210 --> 00:26:50.450 be specialized to support financial inclusion 00:26:50.450 --> 00:26:52.890 in developing economies, not just large retail 00:26:52.890 --> 00:26:55.650 credit. We see a similar state mandate in Pakistan. 00:26:56.240 --> 00:26:58.140 The Electronic Credit Information Bureau, or 00:26:58.140 --> 00:27:00.680 ECI, was established by the State Bank of Pakistan, 00:27:00.960 --> 00:27:05.240 SBP, way back in 1992. And the SBP maintains 00:27:05.240 --> 00:27:08.759 exceptionally strict control. They do. The SBP 00:27:08.759 --> 00:27:11.599 mandates that all financial institutions, banks, 00:27:11.759 --> 00:27:13.880 development institutions, microfinance banks 00:27:13.880 --> 00:27:18.359 must use the ECIB software for monitoring. They 00:27:18.359 --> 00:27:20.519 are required to submit all their borrower records 00:27:20.519 --> 00:27:23.910 online. every single month. That sounds incredibly 00:27:23.910 --> 00:27:26.490 tightly controlled and highly centralized, especially 00:27:26.490 --> 00:27:28.849 compared to the competitive fragmented private 00:27:28.849 --> 00:27:30.910 market we see in the U .S. It's a completely 00:27:30.910 --> 00:27:32.990 different philosophy. What are the practical 00:27:32.990 --> 00:27:35.349 implications of that kind of mandatory government 00:27:35.349 --> 00:27:38.170 use? Well, the immediate implication is uniformity 00:27:38.170 --> 00:27:41.150 and tight state control over identity and risk 00:27:41.150 --> 00:27:43.980 assessment. There's less competition among the 00:27:43.980 --> 00:27:46.079 bureaus, which could lead to less innovation, 00:27:46.339 --> 00:27:48.940 but it ensures that every financial institution 00:27:48.940 --> 00:27:51.880 is working with the exact same complete set of 00:27:51.880 --> 00:27:54.000 data. Which eliminates a lot of that asymmetric 00:27:54.000 --> 00:27:56.640 information problem instantly across the whole 00:27:56.640 --> 00:27:59.079 economy. It centralizes financial risk management 00:27:59.079 --> 00:28:01.640 directly under the purview of the central bank. 00:28:01.799 --> 00:28:03.960 The Philippines also shows this public -private 00:28:03.960 --> 00:28:06.460 tension. It began with a presidential decree 00:28:06.460 --> 00:28:09.779 in 1981, which led to a private, non -profit 00:28:09.779 --> 00:28:13.859 entity. But by 2008, the pendulum swung firmly 00:28:13.859 --> 00:28:16.079 toward the public sector with the Credit Information 00:28:16.079 --> 00:28:19.119 System Act. This created the Credit Information 00:28:19.119 --> 00:28:21.460 Corporation, which is a government -owned and 00:28:21.460 --> 00:28:24.900 controlled credit registry. This constant governmental 00:28:24.900 --> 00:28:27.539 intervention really highlights that in many nations, 00:28:27.640 --> 00:28:30.140 credit data is seen as a vital public utility. 00:28:30.579 --> 00:28:33.859 Yes, necessary for national stability, not just 00:28:33.859 --> 00:28:36.309 a private commodity to be bought and sold. And 00:28:36.309 --> 00:28:38.049 in the Middle East, we look at Saudi Arabia. 00:28:38.410 --> 00:28:41.549 The Saudi Credit Bureau, SIMA, is the first and 00:28:41.549 --> 00:28:44.549 sole licensed national bureau established under 00:28:44.549 --> 00:28:47.130 the supervision of the Saudi Central Bank. The 00:28:47.130 --> 00:28:48.990 history there is interesting because the idea 00:28:48.990 --> 00:28:51.769 for SIMA actually originated in 1998 in coordination 00:28:51.769 --> 00:28:53.930 with the World Bank. Oh, interesting. The World 00:28:53.930 --> 00:28:56.390 Bank often used the implementation of a national 00:28:56.390 --> 00:28:59.329 credit bureau as a prerequisite for fostering 00:28:59.329 --> 00:29:02.490 deep, functional capital markets and moving towards 00:29:02.490 --> 00:29:05.529 a modern, transparent lending economy. CIMA is 00:29:05.529 --> 00:29:08.069 a successful, structured example of that internationally 00:29:08.069 --> 00:29:10.849 recommended development model. And finally, we 00:29:10.849 --> 00:29:13.829 can revisit North America with Canada. They use 00:29:13.829 --> 00:29:16.630 the familiar private bureaus, Equifax Canada 00:29:16.630 --> 00:29:19.589 and TransUnion Canada, but their regulatory structure 00:29:19.589 --> 00:29:23.369 is a unique mix. Canada handles regulation primarily 00:29:23.369 --> 00:29:26.609 on a provincial and territorial level. Most provinces 00:29:26.609 --> 00:29:29.130 have their own specific credit reporting legislation 00:29:29.130 --> 00:29:32.369 detailing what can be collected and how it must 00:29:32.369 --> 00:29:34.829 be disclosed. So if you live in Ontario, you're 00:29:34.829 --> 00:29:37.269 governed by Ontario law, but you also have to 00:29:37.269 --> 00:29:39.809 comply with the federal level. Correct. The bureaus 00:29:39.809 --> 00:29:42.109 must also adhere to the Federal Personal Information 00:29:42.109 --> 00:29:43.789 Procedure. Protection and Electronic Documents 00:29:43.789 --> 00:29:47.029 Act, or PIPEDA, which sets the broad standards 00:29:47.029 --> 00:29:49.509 for handling personal data across the whole country. 00:29:49.630 --> 00:29:51.569 So it's a decentralized system that tries to 00:29:51.569 --> 00:29:53.869 balance federal privacy rights with regional 00:29:53.869 --> 00:29:56.470 economic concerns. We've really established the 00:29:56.470 --> 00:29:58.650 consumer side of the equation, but credit reporting 00:29:58.650 --> 00:30:01.450 is a massive two -sided coin. Businesses are 00:30:01.450 --> 00:30:04.349 also scored, and that side of the industry operates 00:30:04.349 --> 00:30:07.710 with different rules and different legal liabilities. 00:30:07.789 --> 00:30:10.069 Let's look at commercial credit reporting. Right. 00:30:10.130 --> 00:30:12.630 Commercial reporting agencies, they evaluate 00:30:12.630 --> 00:30:15.029 the likelihood of a business paying its creditors. 00:30:15.150 --> 00:30:18.529 It's fundamentally a B2B, a business -to -business 00:30:18.529 --> 00:30:22.259 operation. And while consumer CRAs worry about 00:30:22.259 --> 00:30:24.680 individual privacy and statutory protections 00:30:24.680 --> 00:30:27.539 like the FCRA, commercial agencies primarily 00:30:27.539 --> 00:30:30.880 deal with business -to -business debt. And crucially, 00:30:31.000 --> 00:30:33.900 the laws governing business defamation. The players 00:30:33.900 --> 00:30:36.380 here include specialized agencies like Dun & 00:30:36.380 --> 00:30:38.839 Bradstreet, Cortera, and Experian Commercial. 00:30:39.140 --> 00:30:41.539 And they use entirely different metrics from 00:30:41.539 --> 00:30:44.069 the FICO scores you're used to. Dun & Bradstreet, 00:30:44.130 --> 00:30:46.009 for example, is famous for its Paydex score. 00:30:46.210 --> 00:30:48.609 There's the Experian IntelliScore and the CPR 00:30:48.609 --> 00:30:50.829 score from Corteira. And these metrics are all 00:30:50.829 --> 00:30:53.009 designed to help one business decide whether 00:30:53.009 --> 00:30:56.289 to extend trade credit, say, you know, net 30 00:30:56.289 --> 00:30:58.609 payment terms on a shipment of goods to another 00:30:58.609 --> 00:31:00.589 business. Exactly. There is also the specialized 00:31:00.589 --> 00:31:02.930 structure of the Small Business Financial Exchange, 00:31:03.170 --> 00:31:07.170 or SBFE. The SBFE is a crucial intermediary in 00:31:07.170 --> 00:31:09.849 the U .S. small business lending space. It operates 00:31:09.849 --> 00:31:11.910 as a trade association that gathers and protects 00:31:11.910 --> 00:31:14.319 small business. this payment data from all its 00:31:14.319 --> 00:31:17.480 member institutions. And crucially, they license 00:31:17.480 --> 00:31:20.380 that data only to credit reporting agencies that 00:31:20.380 --> 00:31:22.940 have a specific certified vendor agreement. And 00:31:22.940 --> 00:31:25.140 they strictly prohibit the use of that data for 00:31:25.140 --> 00:31:27.940 marketing purposes. It is solely for risk management. 00:31:28.200 --> 00:31:30.460 They're acting as a guardian for proprietary 00:31:30.460 --> 00:31:33.940 small business data. This B2B context is where 00:31:33.940 --> 00:31:36.799 we find a significant legal landmark in the U 00:31:36.799 --> 00:31:40.960 .S. The 1985 Supreme Court case done in Bradstreet, 00:31:41.059 --> 00:31:45.160 Inc. The Green Moss Builders, Inc. This case 00:31:45.160 --> 00:31:47.440 is absolutely pivotal because it speaks directly 00:31:47.440 --> 00:31:49.819 to the consequences of inaccuracy in the commercial 00:31:49.819 --> 00:31:52.519 sphere. So what did the court hold? The Supreme 00:31:52.519 --> 00:31:54.880 Court held that a credit reporting agency may 00:31:54.880 --> 00:31:57.500 be liable for tort liability for business defamation 00:31:57.500 --> 00:32:00.660 if it was careless in reporting a pending or 00:32:00.660 --> 00:32:02.619 past bankruptcy filing concerning a business 00:32:02.619 --> 00:32:05.200 that is not a public figure. So unlike a private 00:32:05.200 --> 00:32:08.099 consumer who has statutory rights of correction 00:32:08.099 --> 00:32:11.200 under the FCRA. A business have the additional 00:32:11.200 --> 00:32:13.839 protection of defamation law if a false report 00:32:13.839 --> 00:32:16.420 harms their reputation and their financial viability. 00:32:16.599 --> 00:32:18.579 That's right. It demonstrates a much higher legal 00:32:18.579 --> 00:32:20.700 accountability when you're dealing with commercial 00:32:20.700 --> 00:32:23.299 entities versus private individuals. And speaking 00:32:23.299 --> 00:32:25.859 of harm to private individuals, let's pivot to 00:32:25.859 --> 00:32:28.720 the largest and most persistent structural controversy 00:32:28.720 --> 00:32:31.599 in the consumer credit world. The problem of 00:32:31.599 --> 00:32:34.420 systemic inaccuracy. This is where that mechanistic 00:32:34.420 --> 00:32:37.460 system really breaks down and causes real tangible 00:32:37.460 --> 00:32:40.559 harm. We know that a significant percentage of 00:32:40.559 --> 00:32:42.680 credit reports in the U .S. contain inaccuracies. 00:32:42.880 --> 00:32:46.000 The U .S. General Accounting Office, the GAO, 00:32:46.140 --> 00:32:49.099 categorized the causes of these errors into two 00:32:49.099 --> 00:32:52.279 broad buckets. First, the inclusion of incorrect 00:32:52.279 --> 00:32:54.599 information. This is what most people think of. 00:32:54.700 --> 00:32:57.119 Wrong payment dates, wrong account balances, 00:32:57.200 --> 00:33:00.000 or a case of mistaken identity. And second, the 00:33:00.000 --> 00:33:02.559 exclusion of correct information. Which is just 00:33:02.559 --> 00:33:05.019 as damaging. For example, a positive payment 00:33:05.019 --> 00:33:07.119 history that should have been reported but just 00:33:07.119 --> 00:33:09.539 wasn't. And the reasons are manifold, according 00:33:09.539 --> 00:33:12.099 to the GAO. Consumers can provide misinformation, 00:33:12.619 --> 00:33:14.859 the data furnishers provide incorrect or incomplete 00:33:14.859 --> 00:33:18.279 data, or the CRA itself just messes up and applies 00:33:18.279 --> 00:33:20.420 data to the wrong person's file. But the most 00:33:20.420 --> 00:33:22.240 shocking insight from the research that our sources 00:33:22.240 --> 00:33:25.200 highlight is not how the errors occur, but why 00:33:25.200 --> 00:33:28.019 they persist. And it comes down to a fundamental 00:33:28.019 --> 00:33:31.099 lack of economic incentive for the most powerful 00:33:31.099 --> 00:33:33.599 parties in the system. That is a critical insight. 00:33:33.839 --> 00:33:36.440 Think about it. If the banks and the bureaus 00:33:36.440 --> 00:33:38.980 are the ones paying the cost to correct these 00:33:38.980 --> 00:33:41.599 errors. Right. but the benefit of that correction 00:33:41.599 --> 00:33:45.039 flows primarily to you, the consumer, in the 00:33:45.039 --> 00:33:47.680 form of a lower interest rate, then the core 00:33:47.680 --> 00:33:50.819 actors have minimal incentive to pursue perfection. 00:33:51.140 --> 00:33:53.039 It's a disconnect in the cost -benefit analysis. 00:33:53.420 --> 00:33:55.920 Exactly. Our sources cite research showing that, 00:33:56.000 --> 00:33:58.359 and I'm quoting here, the parties that bear the 00:33:58.359 --> 00:34:01.579 costs of correcting errors may not receive much 00:34:01.579 --> 00:34:04.420 benefit from the improvement in accuracy. Why 00:34:04.420 --> 00:34:07.920 spend time and capital achieving 99 .99 % accuracy 00:34:07.920 --> 00:34:11.480 when 99 % accuracy saves you money and generates 00:34:11.480 --> 00:34:13.380 the same level of profit from the institutions 00:34:13.380 --> 00:34:15.980 buying the data? That is a staggering implication. 00:34:16.260 --> 00:34:18.800 It suggests that inaccuracy is not merely an 00:34:18.800 --> 00:34:21.400 administrative oversight. But a structural feature 00:34:21.400 --> 00:34:24.280 of the system's economic model. Wow. It means 00:34:24.280 --> 00:34:26.059 that the most powerful actors in the financial 00:34:26.059 --> 00:34:28.460 system have an actual incentive to maintain a 00:34:28.460 --> 00:34:31.039 suboptimal level of data quality. Which leads 00:34:31.039 --> 00:34:34.760 directly into the second. and perhaps most corrosive 00:34:34.760 --> 00:34:38.000 controversy, the nature of proprietary algorithms 00:34:38.000 --> 00:34:41.619 and what critics label secret law. This is the 00:34:41.619 --> 00:34:44.519 core of the invisible score problem. The mathematical 00:34:44.519 --> 00:34:46.559 formula that's used to calculate your consumer 00:34:46.559 --> 00:34:48.780 credit score, the weights given to different 00:34:48.780 --> 00:34:51.659 types of debt, utilization rates, age of accounts. 00:34:52.019 --> 00:34:54.219 All of it. It's proprietary and considered a 00:34:54.219 --> 00:34:56.719 trade secret in the United States. You know the 00:34:56.719 --> 00:34:59.880 output, the score, but you cannot know the precise 00:34:59.880 --> 00:35:02.960 recipe. And that secrecy creates massive opacity. 00:35:03.710 --> 00:35:06.369 For instance, some CRAs provide what they call 00:35:06.369 --> 00:35:09.190 an educational score to you, the consumer, a 00:35:09.190 --> 00:35:11.289 helpful metric for tracking your general progress. 00:35:11.449 --> 00:35:13.630 Right. But they provide a completely different, 00:35:13.730 --> 00:35:16.710 customized FICO -like score to the actual lender. 00:35:17.420 --> 00:35:19.599 Consumer advocates critically refer to these 00:35:19.599 --> 00:35:22.920 as FACO scores. FACO scores, because they aren't 00:35:22.920 --> 00:35:25.039 the score the lender actually uses to make a 00:35:25.039 --> 00:35:27.260 decision. Which leads to incredible confusion 00:35:27.260 --> 00:35:29.880 and frustration. Let's spend some time unpacking 00:35:29.880 --> 00:35:32.039 that secret law critique, because it sounds heavy, 00:35:32.079 --> 00:35:34.059 but it goes right to the heart of economic opportunity 00:35:34.059 --> 00:35:37.639 and, frankly, due process. It really does. The 00:35:37.639 --> 00:35:39.780 argument is both philosophical and economic. 00:35:40.510 --> 00:35:43.230 When these proprietary algorithms, whose internal 00:35:43.230 --> 00:35:45.869 workings and weights are undisclosed, are used 00:35:45.869 --> 00:35:48.289 as the sole basis to deny people fundamental 00:35:48.289 --> 00:35:51.769 legal rights, like access to employment or insurance 00:35:51.769 --> 00:35:54.750 or necessary credit, then the algorithm itself 00:35:54.750 --> 00:35:58.070 begins to function as a form of law. Precisely. 00:35:58.070 --> 00:36:01.550 The law sets rules. If you commit this violation, 00:36:02.139 --> 00:36:05.000 You face this penalty. But here, you, the individual, 00:36:05.179 --> 00:36:07.239 are facing a penalty, a higher interest rate, 00:36:07.300 --> 00:36:10.079 a job denial based on rules you cannot know. 00:36:10.280 --> 00:36:12.340 You can't manage your credit effectively if you 00:36:12.340 --> 00:36:14.539 don't know the exact mechanism by which your 00:36:14.539 --> 00:36:16.739 behavior is being weighted. You are called upon 00:36:16.739 --> 00:36:18.940 to abide by a system where the rules for success 00:36:18.940 --> 00:36:22.420 or failure are undisclosed. This lack of transparency 00:36:22.420 --> 00:36:25.179 completely impedes effective consumer debt management. 00:36:25.670 --> 00:36:28.329 You can fix the known errors, sure, but you can't 00:36:28.329 --> 00:36:30.269 strategically manage your financial life according 00:36:30.269 --> 00:36:32.650 to a metric whose internal logic is hidden from 00:36:32.650 --> 00:36:35.070 you. And this leads to a pervasive social effect 00:36:35.070 --> 00:36:37.710 that our sources call discrimination by number. 00:36:37.969 --> 00:36:40.550 Right. You aren't being judged by traditional 00:36:40.550 --> 00:36:43.449 protected classes, but by a score that is opaque, 00:36:43.789 --> 00:36:46.130 potentially inaccurate, and whose underlying 00:36:46.130 --> 00:36:49.489 reflection of risk is impossible for you or for 00:36:49.489 --> 00:36:51.860 outside experts to verify. The sources conclude 00:36:51.860 --> 00:36:54.420 that you can't truly define what these repositories 00:36:54.420 --> 00:36:57.139 collect or what the score reflects unless the 00:36:57.139 --> 00:36:59.940 algorithms were publicized. And expert statisticians 00:36:59.940 --> 00:37:02.280 were permitted to examine them for bias or unintended 00:37:02.280 --> 00:37:05.900 consequences or just basic logical flaws. Until 00:37:05.900 --> 00:37:07.739 then, the foundation of the modern financial 00:37:07.739 --> 00:37:10.860 system remains a secret. And finally, let's end 00:37:10.860 --> 00:37:13.320 this section with perhaps the most telling anecdote 00:37:13.320 --> 00:37:16.539 of self -interest in the whole industry. Something 00:37:16.539 --> 00:37:18.900 that proves the bureaus are well aware of the 00:37:18.900 --> 00:37:21.539 potential consequences of inaccuracy. And that 00:37:21.539 --> 00:37:24.239 is the VIP database. This is an extraordinary 00:37:24.239 --> 00:37:27.239 specific detail that just crystallizes the issue 00:37:27.239 --> 00:37:29.900 of fairness. According to experts involved in 00:37:29.900 --> 00:37:32.539 litigation against CRAs, some agencies in the 00:37:32.539 --> 00:37:35.239 U .S. maintain a specialized VIP database of 00:37:35.239 --> 00:37:37.380 special consumers. Who are we talking about here? 00:37:37.420 --> 00:37:40.639 Who gets this VIP treatment? Members of Congress, 00:37:40.860 --> 00:37:43.619 high level judges, well -known actors, celebrities 00:37:43.619 --> 00:37:46.480 and other highly influential public figures. 00:37:46.699 --> 00:37:49.639 These individuals are not subjected to the normal, 00:37:49.780 --> 00:37:52.639 automated, often error prone dispute process. 00:37:52.980 --> 00:37:55.380 Instead, their reports are subjected to special 00:37:55.380 --> 00:37:58.280 administration by the Bureau to ensure their 00:37:58.280 --> 00:38:00.800 credit report is flawlessly accurate and not 00:38:00.800 --> 00:38:03.420 negatively handled. And the rationale for this 00:38:03.420 --> 00:38:05.500 special treatment is pure self -preservation 00:38:05.500 --> 00:38:07.860 for the industry. The sources state that these 00:38:07.860 --> 00:38:10.239 high -powered individuals could cause significant 00:38:10.239 --> 00:38:12.480 problems for the bureaus, including negative 00:38:12.480 --> 00:38:14.980 publicity and legislative action which could 00:38:14.980 --> 00:38:17.409 adversely affect the industry. So they fix the 00:38:17.409 --> 00:38:19.650 problem for the people who are most likely to 00:38:19.650 --> 00:38:22.170 legislate against them while allowing the systemic 00:38:22.170 --> 00:38:25.190 issues of inaccuracy and proprietary formulas 00:38:25.190 --> 00:38:27.670 to persist for the average consumer who lacks 00:38:27.670 --> 00:38:30.309 the resources or political pull to demand perfection. 00:38:30.710 --> 00:38:33.869 It highlights in the starkest terms the disparity 00:38:33.869 --> 00:38:36.309 in access to an accurate financial identity. 00:38:36.590 --> 00:38:39.510 So we've traced the history from coded reference 00:38:39.510 --> 00:38:42.769 books to these complex proprietary algorithms, 00:38:42.989 --> 00:38:45.170 and we've seen how the credit bureau evolved 00:38:45.170 --> 00:38:48.219 from an. informal merchant network into a global 00:38:48.219 --> 00:38:50.659 system, whether state -sanctioned or privately 00:38:50.659 --> 00:38:53.360 driven, that's critical to every modern economy. 00:38:53.619 --> 00:38:56.239 We've seen its foundational purpose to reduce 00:38:56.239 --> 00:38:58.960 asymmetric information and the immense power 00:38:58.960 --> 00:39:01.659 this system holds, determining everything from 00:39:01.659 --> 00:39:05.820 microfinance access in India to insurance premiums 00:39:05.820 --> 00:39:08.110 in the U .S. But the discussion really reveals 00:39:08.110 --> 00:39:10.670 that the system is plagued by some serious structural 00:39:10.670 --> 00:39:13.909 flaws, particularly that economic incentive against 00:39:13.909 --> 00:39:16.289 pursuing perfect data quality. And that leads 00:39:16.289 --> 00:39:18.789 us back to the unique economic architecture of 00:39:18.789 --> 00:39:21.289 this entire industry, which the source material 00:39:21.289 --> 00:39:24.139 labels the undefined triangle. The dynamics here 00:39:24.139 --> 00:39:26.559 are highly counterintuitive to any known market 00:39:26.559 --> 00:39:28.599 model. Okay. The undefined triangle. Let's break 00:39:28.599 --> 00:39:30.199 that down. Well, think about the three corners. 00:39:30.280 --> 00:39:32.260 First, you have the sponsoring industries, finance, 00:39:32.480 --> 00:39:35.840 banking, insurance, which pay the CRAs massive 00:39:35.840 --> 00:39:38.239 amounts of money to access and process the information. 00:39:38.539 --> 00:39:41.039 Corner number one. Then you have the CRAs themselves, 00:39:41.360 --> 00:39:44.380 which aggregate and sell the product. That's 00:39:44.380 --> 00:39:47.099 corner two. And then you have the consumer. Right. 00:39:47.420 --> 00:39:49.780 And the consumer, who is the subject of the data, 00:39:49.940 --> 00:39:53.539 is also often paying the CRAs, either through 00:39:53.539 --> 00:39:56.099 monitoring services or by buying their own score 00:39:56.099 --> 00:39:58.679 and report. So it's a loop where two different 00:39:58.679 --> 00:40:01.420 parties are paying for the service based on the 00:40:01.420 --> 00:40:03.599 utility they receive. But the utility of the 00:40:03.599 --> 00:40:05.920 consumer is extremely difficult to calculate 00:40:05.920 --> 00:40:08.659 or even define. Why is the consumer's utility 00:40:08.659 --> 00:40:11.539 so hard to define in this specific structure? 00:40:11.800 --> 00:40:14.579 Because the consumer... who is paying for the 00:40:14.579 --> 00:40:18.119 service, is simultaneously the raw material and 00:40:18.119 --> 00:40:20.880 the party with insufficient recourse to correct 00:40:20.880 --> 00:40:22.840 mistakes about themselves. So if your report 00:40:22.840 --> 00:40:25.940 is inaccurate, your utility, your cost of borrowing 00:40:25.940 --> 00:40:28.639 is negatively impacted, but you are still forced 00:40:28.639 --> 00:40:31.300 to participate in this triangle. Exactly. The 00:40:31.300 --> 00:40:33.639 financial relationship between consumers, credit 00:40:33.639 --> 00:40:35.500 reporters, and sponsoring industries remains, 00:40:35.619 --> 00:40:38.500 to this day, undefined by any established economic 00:40:38.500 --> 00:40:41.400 model. It's an economy that runs on private data 00:40:41.400 --> 00:40:44.340 about public participation, but without the standard 00:40:44.340 --> 00:40:47.679 market incentives for quality. So what does this 00:40:47.679 --> 00:40:49.940 all mean for you, the person who lives under 00:40:49.940 --> 00:40:52.699 the reign of this invisible score? Since the 00:40:52.699 --> 00:40:54.940 source material highlights that an economic model 00:40:54.940 --> 00:40:57.079 to describe this industry has not been attempted, 00:40:57.340 --> 00:40:59.559 and the dynamics between the key players remain 00:40:59.559 --> 00:41:02.280 undefined, we want to leave you with this final 00:41:02.280 --> 00:41:05.030 provocative thought. If the score that ultimately 00:41:05.030 --> 00:41:07.469 determines your access to opportunity, a car, 00:41:07.630 --> 00:41:10.389 a job, a home, is calculated by a proprietary 00:41:10.389 --> 00:41:13.550 formula, and the major players who create and 00:41:13.550 --> 00:41:16.449 use that score have a demonstrable economic incentive 00:41:16.449 --> 00:41:19.409 to not fix the underlying errors, how can an 00:41:19.409 --> 00:41:22.150 individual subject to this score truly participate 00:41:22.150 --> 00:41:24.469 in a fair and transparent financial system when 00:41:24.469 --> 00:41:27.070 the essential law they must abide by remains, 00:41:27.289 --> 00:41:30.429 by design, secret? That fundamental paradox is 00:41:30.429 --> 00:41:32.570 the unsolved challenge of modern consumer data.