WEBVTT 00:00:00.000 --> 00:00:02.439 Welcome back to the Deep Dive. Today we are focusing 00:00:02.439 --> 00:00:04.580 on something that really is the mathematical 00:00:04.580 --> 00:00:07.379 architecture of your financial life. It is. We're 00:00:07.379 --> 00:00:10.000 talking about a single three digit number that 00:00:10.000 --> 00:00:12.320 can dictate the cost of your car, your ability 00:00:12.320 --> 00:00:15.839 to get a house, and believe it or not, sometimes 00:00:15.839 --> 00:00:18.719 even your ability to land a job. It's the ultimate 00:00:18.719 --> 00:00:21.940 gatekeeper in the modern economy. And it feels... 00:00:22.440 --> 00:00:24.539 Well, it feels pretty mysterious to a lot of 00:00:24.539 --> 00:00:26.539 people. It does. And that's why we're doing this. 00:00:26.620 --> 00:00:29.559 A really intensive deep dive into credit history, 00:00:29.780 --> 00:00:33.240 the reports, and that enigmatic credit score. 00:00:33.500 --> 00:00:36.500 For so many, it's like this shadowy figure making 00:00:36.500 --> 00:00:38.960 these huge decisions. But it's not magic. Not 00:00:38.960 --> 00:00:40.820 at all. We've gone through the research, the 00:00:40.820 --> 00:00:44.259 actual laws, the proprietary algorithms to pull 00:00:44.259 --> 00:00:46.460 back the curtain. Absolutely. We've got a heavy 00:00:46.460 --> 00:00:48.539 stack of sources right here detailing everything 00:00:48.539 --> 00:00:50.640 from the Fair Credit Reporting Act to the actual 00:00:50.640 --> 00:00:53.439 FICO weighting formula. So our mission for you, 00:00:53.539 --> 00:00:55.439 the listener, the learner, is pretty simple. 00:00:55.619 --> 00:00:58.240 We won't give you a shortcut. Exactly. A shortcut 00:00:58.240 --> 00:01:01.899 to understanding this whole apparatus. We want 00:01:01.899 --> 00:01:04.040 you to walk away from this not just knowing your 00:01:04.040 --> 00:01:06.200 score, but really getting the mathematical leverage 00:01:06.200 --> 00:01:08.540 points that let you manage it. And crucially, 00:01:08.599 --> 00:01:11.140 understanding the tangible impact it has every 00:01:11.140 --> 00:01:14.159 time you apply for, well, anything. Okay, let's 00:01:14.159 --> 00:01:15.840 get into it. Okay, let's unpack this. Before 00:01:15.840 --> 00:01:17.760 we get into all the percentages and the risk 00:01:17.760 --> 00:01:19.959 models, we have to clear something up. People 00:01:19.959 --> 00:01:22.739 tend to use three terms interchangeably. History, 00:01:22.980 --> 00:01:25.489 report, and score. And they're not the same thing 00:01:25.489 --> 00:01:27.230 at all. They're fundamentally distinct elements. 00:01:27.549 --> 00:01:29.890 Let's start at the bottom layer. Credit history. 00:01:30.569 --> 00:01:34.409 What exactly is that? The credit history is the 00:01:34.409 --> 00:01:38.450 foundational record. Think of it like a raw chronological 00:01:38.450 --> 00:01:41.510 diary of your life as a borrower. Just the facts. 00:01:41.709 --> 00:01:43.909 Just the facts. Did you pay on time? Did you 00:01:43.909 --> 00:01:46.189 default? Did you borrow too much against your 00:01:46.189 --> 00:01:48.709 limit? It's the factual timeline. Right. So that's 00:01:48.709 --> 00:01:50.510 the activity. And when you take all that activity 00:01:50.510 --> 00:01:53.989 and compile it, you get the credit report. Precisely. 00:01:54.489 --> 00:01:56.989 The credit report is the document that comes 00:01:56.989 --> 00:01:59.650 out of that history. And the data is gathered 00:01:59.650 --> 00:02:03.209 from, well, everywhere. Banks, credit card companies, 00:02:03.390 --> 00:02:05.129 collection agencies, sometimes even government 00:02:05.129 --> 00:02:07.530 records for things like public judgments. So 00:02:07.530 --> 00:02:11.270 it's the comprehensive, organized list of every 00:02:11.270 --> 00:02:13.189 account, every balance, every payment you've 00:02:13.189 --> 00:02:15.349 made or missed. That's it. And finally, that 00:02:15.349 --> 00:02:18.250 brings us to the infamous three -digit number, 00:02:18.469 --> 00:02:21.509 the credit score. The score is the numerical 00:02:21.509 --> 00:02:24.930 output. It's what you get when a complex mathematical 00:02:24.930 --> 00:02:27.530 algorithm in the U .S., that's usually the FICO 00:02:27.530 --> 00:02:30.409 model, is applied to all that raw data in your 00:02:30.409 --> 00:02:32.449 report. And it's so critical to remember what 00:02:32.449 --> 00:02:35.229 its function is. Yes. Its only purpose is to 00:02:35.229 --> 00:02:37.990 predict future delinquency. That's it. It's not 00:02:37.990 --> 00:02:40.389 judging your character. No. It's just assessing 00:02:40.389 --> 00:02:42.129 the statistical probability that you're going 00:02:42.129 --> 00:02:44.250 to be a risk in the future. That distinction 00:02:44.250 --> 00:02:47.580 is paramount. History is what you did. The report 00:02:47.580 --> 00:02:49.979 is the document of what you did, and the score 00:02:49.979 --> 00:02:52.719 is the math predicting what you'll do next. So 00:02:52.719 --> 00:02:55.340 how do lenders actually use this thing? Okay, 00:02:55.419 --> 00:02:58.479 so when you apply for credit, a mortgage, a car 00:02:58.479 --> 00:03:02.479 loan, a credit card, the lender sends your information 00:03:02.479 --> 00:03:04.860 to one of the credit bureaus, you know, Equifax, 00:03:04.900 --> 00:03:06.900 Experian, or TransUnion. And they're trying to 00:03:06.900 --> 00:03:08.860 figure out two things about you. Two things. 00:03:08.939 --> 00:03:11.280 The first one is pretty straightforward. Your 00:03:11.280 --> 00:03:14.219 ability to repay. which is mostly just your income, 00:03:14.360 --> 00:03:16.099 your job, that kind of thing. And the second, 00:03:16.219 --> 00:03:18.360 which is where this three -digit score comes 00:03:18.360 --> 00:03:21.039 in, is your willingness to repay. And that's 00:03:21.039 --> 00:03:22.879 all based on the track record in the report. 00:03:23.280 --> 00:03:26.460 Entirely. Is there a history of consistent on 00:03:26.460 --> 00:03:30.819 -time payments, or is it inconsistent? That track 00:03:30.819 --> 00:03:34.120 record is what signals willingness. And our research 00:03:34.120 --> 00:03:36.360 really zeroed in on what breaks that signal. 00:03:36.439 --> 00:03:39.240 It's not about, say, paying extra. It's all about 00:03:39.240 --> 00:03:42.069 not missing payments. That's such a crucial insight. 00:03:42.389 --> 00:03:45.430 If you overpay one month, the system doesn't 00:03:45.430 --> 00:03:47.789 care. It doesn't see that as an offset for a 00:03:47.789 --> 00:03:49.909 payment you missed six months ago. No bonus points 00:03:49.909 --> 00:03:52.930 for being good. No. The system is entirely geared 00:03:52.930 --> 00:03:55.650 to penalize inconsistency. It doesn't reward 00:03:55.650 --> 00:03:58.270 these sporadic bursts of financial health. It 00:03:58.270 --> 00:04:00.770 rewards sustained, almost boring consistency. 00:04:01.250 --> 00:04:03.849 So the moment a payment is late. The risk model 00:04:03.849 --> 00:04:07.740 reacts, and it reacts disproportionately. Because 00:04:07.740 --> 00:04:10.000 statistically, a missed payment, especially a 00:04:10.000 --> 00:04:13.039 recent one, is the most powerful predictor of 00:04:13.039 --> 00:04:15.259 a future default. That really sets the stage 00:04:15.259 --> 00:04:17.740 perfectly. Because if this whole system is built 00:04:17.740 --> 00:04:21.139 on data, we have to ask, how good is that data? 00:04:21.839 --> 00:04:25.339 Which brings us to Section 1, the accuracy problem 00:04:25.339 --> 00:04:28.170 and the regulations designed to police it. This 00:04:28.170 --> 00:04:30.850 is a huge point of tension. If you ask most people, 00:04:30.930 --> 00:04:32.870 they'll say credit reports are just riddled with 00:04:32.870 --> 00:04:35.189 errors. Right. The consumer perception is that 00:04:35.189 --> 00:04:37.610 they're full of incorrect balances, payments 00:04:37.610 --> 00:04:40.470 marked late that were on time, even accounts 00:04:40.470 --> 00:04:42.889 that aren't theirs. But the industry view, at 00:04:42.889 --> 00:04:44.550 least according to the sources we looked at, 00:04:44.589 --> 00:04:47.029 presents a totally different picture. A very 00:04:47.029 --> 00:04:49.170 different picture. The credit bureaus maintain 00:04:49.170 --> 00:04:52.370 pretty rigorously that their data is highly accurate. 00:04:52.829 --> 00:04:54.810 They'll cite these massive internal studies. 00:04:55.009 --> 00:04:57.790 One of them looked at 52 million credit reports 00:04:57.790 --> 00:05:00.649 to claim a minimal error rate. And when they 00:05:00.649 --> 00:05:02.810 testified before Congress, the industry reps 00:05:02.810 --> 00:05:06.050 were specific. They said less than 2 % of reports 00:05:06.050 --> 00:05:08.509 that led to a consumer dispute actually had data 00:05:08.509 --> 00:05:11.149 deleted because of a verified error. Which sounds 00:05:11.149 --> 00:05:13.850 incredibly low. It's a staggering contradiction 00:05:13.850 --> 00:05:16.029 to what you hear from people every day. It is, 00:05:16.029 --> 00:05:18.029 but... Let's connect that number back to the 00:05:18.029 --> 00:05:19.829 scale of the system because the tension is still 00:05:19.829 --> 00:05:23.470 there. If you've got, what, 200 million active 00:05:23.470 --> 00:05:26.730 credit reports in the U .S. alone, 2 percent 00:05:26.730 --> 00:05:30.529 is still 4 million reports with errors bad enough 00:05:30.529 --> 00:05:32.689 that they had to be deleted. That's a great point. 00:05:32.870 --> 00:05:35.509 Whether the error rate is technically high or 00:05:35.509 --> 00:05:38.850 low, the impact of an error is so significant 00:05:38.850 --> 00:05:41.800 that Congress felt it had to step in. And that 00:05:41.800 --> 00:05:43.839 led to the laws that govern the consumer dispute 00:05:43.839 --> 00:05:46.060 process in the U .S. Right. The Fair Credit Reporting 00:05:46.060 --> 00:05:50.439 Act or FCRA. So if a listener finds an error 00:05:50.439 --> 00:05:52.980 on their report, let's say a late payment they 00:05:52.980 --> 00:05:55.699 know they made on time, what's the legally mandated 00:05:55.699 --> 00:05:58.459 process for getting it fixed? OK, so once you, 00:05:58.620 --> 00:06:01.319 the consumer, officially file a dispute, the 00:06:01.319 --> 00:06:04.279 credit bureau is on the clock. They have a legal 00:06:04.279 --> 00:06:07.240 mandate of 30 calendar days to investigate and 00:06:07.240 --> 00:06:10.060 verify that piece of data. What does an investigation 00:06:10.060 --> 00:06:12.500 entail? They don't just take your word for it. 00:06:12.660 --> 00:06:15.160 No, they have to contact the data furnisher. 00:06:15.500 --> 00:06:17.860 That's the bank or the creditor who reported 00:06:17.860 --> 00:06:20.980 the information in the first place. That bank 00:06:20.980 --> 00:06:24.779 then has to confirm or deny the accuracy of the 00:06:24.779 --> 00:06:26.879 item you're disputing. And if they don't confirm 00:06:26.879 --> 00:06:29.620 it within that 30 -day window. Or if they confirm 00:06:29.620 --> 00:06:31.949 it's incorrect. then the credit bureau is required 00:06:31.949 --> 00:06:35.370 by law to either delete or correct the information 00:06:35.370 --> 00:06:38.490 immediately. And you get notified of what happened. 00:06:38.569 --> 00:06:40.560 Yes. They have to tell you the outcome. And the 00:06:40.560 --> 00:06:42.699 sources suggest that, you know, despite all the 00:06:42.699 --> 00:06:44.899 anxiety around this, the process is often pretty 00:06:44.899 --> 00:06:47.579 efficient. It is, surprisingly. The data shows 00:06:47.579 --> 00:06:50.160 over 70 percent of these disputes are actually 00:06:50.160 --> 00:06:53.259 resolved within 14 days, much faster than the 00:06:53.259 --> 00:06:55.240 30 days they're allowed. And the Federal Trade 00:06:55.240 --> 00:06:57.240 Commission even noted that people seem pretty 00:06:57.240 --> 00:06:59.600 happy with the results. One big bureau found 00:06:59.600 --> 00:07:02.699 95 percent of consumers who filed a dispute were 00:07:02.699 --> 00:07:05.399 satisfied with the outcome. Which raises an interesting 00:07:05.399 --> 00:07:09.199 question. If 95 % are satisfied and 70 % are 00:07:09.199 --> 00:07:12.040 resolved in two weeks, why does this perception 00:07:12.040 --> 00:07:16.360 of inaccuracy and total frustration persist so 00:07:16.360 --> 00:07:18.860 strongly? I think it's likely because the stakes 00:07:18.860 --> 00:07:21.939 are so high. Right. When an error does slip through, 00:07:22.120 --> 00:07:25.779 its impact can be catastrophic. It could be the 00:07:25.779 --> 00:07:27.399 thing that stops you from getting a mortgage, 00:07:27.560 --> 00:07:30.790 or it could cost you a job. It's the severity 00:07:30.790 --> 00:07:33.610 of the consequences, I think, not necessarily 00:07:33.610 --> 00:07:36.209 the frequency of the errors that fuels all that 00:07:36.209 --> 00:07:39.389 public anxiety. And the stakes have never been 00:07:39.389 --> 00:07:42.110 higher. Which brings us to the importance of 00:07:42.110 --> 00:07:44.290 the credit report in determining the actual cost 00:07:44.290 --> 00:07:47.310 of your life. Yes. If we connect this to the 00:07:47.310 --> 00:07:50.189 bigger picture, the report's influence has just 00:07:50.189 --> 00:07:52.709 exploded because of the widespread adoption of 00:07:52.709 --> 00:07:55.310 risk -based pricing. What does that mean exactly? 00:07:55.410 --> 00:07:57.310 Well, before scoring models became universal, 00:07:57.629 --> 00:08:00.009 lenders might have had broader criteria, maybe 00:08:00.009 --> 00:08:02.069 more standardized terms for everyone. But now 00:08:02.069 --> 00:08:05.170 it's intensely personalized. It is. In many, 00:08:05.209 --> 00:08:08.189 many cases today, the credit report and the score 00:08:08.189 --> 00:08:10.970 that comes from it is the sole element used to 00:08:10.970 --> 00:08:13.449 determine your annual percentage rate, your APR. 00:08:13.529 --> 00:08:15.769 And not just the APR. No, the grace period on 00:08:15.769 --> 00:08:17.709 a credit card, other contractual obligations 00:08:17.709 --> 00:08:20.569 of the loan. It all comes from that one number. 00:08:20.970 --> 00:08:22.790 So this number doesn't just grant you access 00:08:22.790 --> 00:08:25.329 to credit. It determines the cost of that access. 00:08:26.050 --> 00:08:28.529 Precisely. The difference between a high score 00:08:28.529 --> 00:08:31.990 and a low score could mean you're paying 5 % 00:08:31.990 --> 00:08:34.690 on a car loan or you're paying 25%. We're talking 00:08:34.690 --> 00:08:36.549 about a difference of thousands of dollars in 00:08:36.549 --> 00:08:38.809 interest over the life of the loan. Exactly. 00:08:38.830 --> 00:08:41.889 A low score doesn't just put you in a high -risk 00:08:41.889 --> 00:08:46.299 category. It means you are financially... subsidizing 00:08:46.299 --> 00:08:49.179 the risk of everyone else in that category by 00:08:49.179 --> 00:08:51.580 carrying a dramatically higher rate. And understanding 00:08:51.580 --> 00:08:54.299 that link between the score and the cost is just 00:08:54.299 --> 00:08:57.879 foundational. It is. Which is the perfect segue 00:08:57.879 --> 00:09:00.120 into the formula itself. This is the central 00:09:00.120 --> 00:09:02.740 piece of this deep dive, the mathematical engine 00:09:02.740 --> 00:09:05.370 behind the score. We're going to break down the 00:09:05.370 --> 00:09:07.129 FICO model. Which is the acknowledged standard 00:09:07.129 --> 00:09:09.389 in the U .S., Canada, and a lot of other places. 00:09:09.590 --> 00:09:12.190 The general range for a FICO score is 300 to 00:09:12.190 --> 00:09:15.149 850. And while there are different versions of 00:09:15.149 --> 00:09:17.629 the model for, say, mortgages or auto loans, 00:09:17.809 --> 00:09:20.830 the core architecture is always built on five 00:09:20.830 --> 00:09:23.509 factors with fixed percentage weights. And these 00:09:23.509 --> 00:09:25.070 weights are, you know, they're non -negotiable 00:09:25.070 --> 00:09:27.129 facts about how the model works. Let's start 00:09:27.129 --> 00:09:29.370 with the biggest one, the most critical factor, 00:09:29.549 --> 00:09:33.259 contributing a colossal 35%. Payment history. 00:09:33.480 --> 00:09:36.159 This is the foundation. Absolute consistency 00:09:36.159 --> 00:09:39.500 matters most here. Any record of negative information 00:09:39.500 --> 00:09:42.539 will just drastically lower your score. By negative 00:09:42.539 --> 00:09:44.899 information, we mean? We're talking bankruptcies, 00:09:45.019 --> 00:09:47.899 accounts and collections, charge -offs, foreclosures, 00:09:47.960 --> 00:09:51.159 or just any late or missed payments, even if 00:09:51.159 --> 00:09:53.500 it's just 30 days past due. And this is where 00:09:53.500 --> 00:09:55.519 the research gave us some really essential nuance. 00:09:56.120 --> 00:09:58.340 FICO doesn't treat all of these negative items 00:09:58.340 --> 00:10:01.039 equally within that 35%. No, it weighs three 00:10:01.039 --> 00:10:04.580 things. The severity, the age, and the prevalence 00:10:04.580 --> 00:10:06.159 of the negative items. Okay, let's break that 00:10:06.159 --> 00:10:08.860 down. Severity. A 30 -day late payment is bad. 00:10:09.299 --> 00:10:12.779 A 60 -day late payment is worse. And a 90 -day 00:10:12.779 --> 00:10:15.700 late payment is catastrophic to your score. The 00:10:15.700 --> 00:10:18.460 model assigns this sort of exponential risk to 00:10:18.460 --> 00:10:20.700 how long you've been delinquent. It sees a 90 00:10:20.700 --> 00:10:23.320 -day late as a signal of extreme financial stress. 00:10:23.620 --> 00:10:25.940 Exactly. Now, what about the age of the item? 00:10:26.039 --> 00:10:28.179 How does time heal the score mathematically? 00:10:28.809 --> 00:10:31.490 This is a huge one. The sources confirm a crucial 00:10:31.490 --> 00:10:34.789 distinction. More recent negative items are considered 00:10:34.789 --> 00:10:37.950 much worse than older ones. The model has what 00:10:37.950 --> 00:10:40.750 you could call a memory decay function. Right. 00:10:40.830 --> 00:10:43.230 So a 30 -day late payment from six months ago 00:10:43.230 --> 00:10:45.870 might carry 10 times the negative weight of a 00:10:45.870 --> 00:10:48.450 30 -day late from five years ago. And after seven 00:10:48.450 --> 00:10:51.149 years, most of these negative items, late payments, 00:10:51.289 --> 00:10:53.649 collections, charge -offs, they just disappear 00:10:53.649 --> 00:10:55.690 from your report entirely. Their weight becomes 00:10:55.690 --> 00:10:58.100 zero. So if a listener is going through a tough 00:10:58.100 --> 00:11:00.679 time financially, the absolute number one priority 00:11:00.679 --> 00:11:03.460 has to be preventing new recent delinquencies. 00:11:03.679 --> 00:11:05.799 Absolutely, because those are the ultimate score 00:11:05.799 --> 00:11:08.639 killers. The age factor means you can recover, 00:11:08.799 --> 00:11:11.879 but it's slow. It requires sustained, recent, 00:11:11.960 --> 00:11:14.500 perfect behavior to offset the severe negative 00:11:14.500 --> 00:11:17.360 weight of that new problem. Okay. Moving on to 00:11:17.360 --> 00:11:19.820 the silver medalist, contributing 30 % of your 00:11:19.820 --> 00:11:23.019 score, debt. Or, more accurately, the amount 00:11:23.019 --> 00:11:25.279 of debt you owe. This is where strategic management 00:11:25.279 --> 00:11:28.240 can really, really pay off. This category is 00:11:28.240 --> 00:11:32.279 dominated by one single pivotal metric. The revolving 00:11:32.279 --> 00:11:34.320 utilization ratio. Let's break down the math 00:11:34.320 --> 00:11:36.340 on that. What is it? It's a simple calculation, 00:11:36.460 --> 00:11:38.960 but it's powerful. You divide your total credit 00:11:38.960 --> 00:11:40.860 card balances by your total credit card limits. 00:11:41.370 --> 00:11:45.149 So if you have $10 ,000 in available credit across 00:11:45.149 --> 00:11:47.950 all your cards and you're carrying a $1 ,000 00:11:47.950 --> 00:11:52.490 balance, your utilization is 10%. And the research 00:11:52.490 --> 00:11:55.490 is crystal clear. The higher that percentage 00:11:55.490 --> 00:11:59.070 goes, the lower your score will be. Why? What 00:11:59.070 --> 00:12:01.549 does it signal to the model? It's a direct risk 00:12:01.549 --> 00:12:04.870 indicator. If you're maxed out using 95 % of 00:12:04.870 --> 00:12:07.210 your available credit, the scoring model sees 00:12:07.210 --> 00:12:09.090 you as high risk because you have basically no 00:12:09.090 --> 00:12:11.700 financial cushion left. And the research provided 00:12:11.700 --> 00:12:14.139 some really specific guidance on this. What's 00:12:14.139 --> 00:12:17.139 the optimal range for utilization? To maximize 00:12:17.139 --> 00:12:19.559 your score in this 30 % category, you really 00:12:19.559 --> 00:12:22.039 want to keep your utilization ratio below 10%. 00:12:22.039 --> 00:12:25.120 Below 10%. Yes. Once you cross the 30 % threshold, 00:12:25.379 --> 00:12:27.360 the scoring penalty becomes pretty significant 00:12:27.360 --> 00:12:30.059 and sharp. But the good news is you can recover 00:12:30.059 --> 00:12:32.279 from high utilization quickly, unlike a late 00:12:32.279 --> 00:12:34.980 payment. But yeah, below 10 % is the gold standard. 00:12:35.139 --> 00:12:37.840 This directly connects to that common warning 00:12:37.840 --> 00:12:40.659 about closing old credit cards. Someone pays 00:12:40.659 --> 00:12:43.299 off a card and they think they're being responsible 00:12:43.299 --> 00:12:45.879 by closing the account. Why does that often hurt 00:12:45.879 --> 00:12:48.389 their score? because it blows up their utilization 00:12:48.389 --> 00:12:51.690 ratio. When you close that account, you instantly 00:12:51.690 --> 00:12:55.169 reduce your total available credit limit. That's 00:12:55.169 --> 00:12:57.649 the denominator in the calculation. So unless 00:12:57.649 --> 00:12:59.889 you have zero balances on all your other cards, 00:13:00.029 --> 00:13:02.669 your utilization percentage automatically goes 00:13:02.669 --> 00:13:05.049 up, which often leads to a score drop. It's all 00:13:05.049 --> 00:13:07.590 a ratio game. Keep the limits high and the balances 00:13:07.590 --> 00:13:10.870 low. Exactly. The best advice is usually to keep 00:13:10.870 --> 00:13:13.399 those old paid -off cards open. You know, put 00:13:13.399 --> 00:13:15.639 them in a drawer, use them for a small purchase 00:13:15.639 --> 00:13:17.860 every six months to keep them active, but keep 00:13:17.860 --> 00:13:20.740 that credit line open. Within this debt category, 00:13:21.039 --> 00:13:23.799 the sources break down three distinct types of 00:13:23.799 --> 00:13:26.440 debt that the models look at. Right. First and 00:13:26.440 --> 00:13:29.100 most important is revolving debt. This is your 00:13:29.100 --> 00:13:31.940 unsecured credit cards, retail cards, gas cards. 00:13:32.159 --> 00:13:34.080 And it's most important because it's not tied 00:13:34.080 --> 00:13:36.940 to an asset. Exactly. It represents the highest 00:13:36.940 --> 00:13:39.139 risk to the lender, so it carries the most weight 00:13:39.139 --> 00:13:42.000 in that utilization calculation. Okay. Second 00:13:42.000 --> 00:13:44.779 is installment debt. This is your fixed term, 00:13:44.860 --> 00:13:48.179 fixed payment stuff, mortgages, auto loans, student 00:13:48.179 --> 00:13:50.220 loans. How important is that? It's definitely 00:13:50.220 --> 00:13:52.620 considered, and managing it well helps your score, 00:13:52.799 --> 00:13:56.360 but it's a distant second in importance to revolving 00:13:56.360 --> 00:13:59.500 debt. The key is a collateral. The loan is secured 00:13:59.500 --> 00:14:02.340 by a house or a car, so you have a very strong 00:14:02.340 --> 00:14:04.779 motivation to keep paying. The risk is lower. 00:14:04.980 --> 00:14:08.210 And finally, a less common type. Open debt. Right, 00:14:08.330 --> 00:14:09.830 like some traditional charge cards where you 00:14:09.830 --> 00:14:11.330 have to pay the balance in full every month. 00:14:11.450 --> 00:14:14.029 How does FICO see that? Well, older FICO models 00:14:14.029 --> 00:14:16.429 sometimes looped it in with revolving debt, which 00:14:16.429 --> 00:14:18.830 is a problem. But the good news is that the newer 00:14:18.830 --> 00:14:21.789 FICO versions tend to exclude open debt from 00:14:21.789 --> 00:14:24.309 the revolving utilization calculation. That's 00:14:24.309 --> 00:14:26.009 a good nuance to know. So we've covered the two 00:14:26.009 --> 00:14:28.549 heavy hitters. That's 65 % of your score right 00:14:28.549 --> 00:14:31.129 there. Let's move to the third factor, which 00:14:31.129 --> 00:14:34.830 contributes 15%. And that is time in file or 00:14:34.830 --> 00:14:37.789 the age of your credit. Simple concept here. 00:14:38.389 --> 00:14:41.289 Longevity signals stability. The older your credit 00:14:41.289 --> 00:14:43.470 report is, the more data points the model has 00:14:43.470 --> 00:14:45.610 to work with and the more stable you look as 00:14:45.610 --> 00:14:47.750 a borrower. And the algorithm actually makes 00:14:47.750 --> 00:14:50.570 two distinct age calculations here, right? Correct. 00:14:50.649 --> 00:14:52.950 First, it looks at the age of the entire credit 00:14:52.950 --> 00:14:55.669 file, which is set by the date opened of your 00:14:55.669 --> 00:14:58.629 single oldest account. Okay. And second, it calculates 00:14:58.629 --> 00:15:01.610 the average age of all your accounts, both open 00:15:01.610 --> 00:15:04.360 and closed, that are still on your report. Which, 00:15:04.379 --> 00:15:06.740 again, reinforces that advice about not closing 00:15:06.740 --> 00:15:09.679 your oldest card. It's a double whammy if you 00:15:09.679 --> 00:15:12.320 do. You hurt your utilization, and you could 00:15:12.320 --> 00:15:14.820 drag down your average account age, hitting two 00:15:14.820 --> 00:15:17.980 major scoring categories at once. A compounding 00:15:17.980 --> 00:15:20.580 negative effect. Definitely. Stability and age 00:15:20.580 --> 00:15:23.820 are your best friends here. Okay. Next up, contributing 00:15:23.820 --> 00:15:29.149 10 % is account diversity. This one is pretty 00:15:29.149 --> 00:15:31.149 straightforward, but it's important for getting 00:15:31.149 --> 00:15:33.110 into those really high score ranges like 800 00:15:33.110 --> 00:15:35.549 plus. So what are they looking for? Lenders just 00:15:35.549 --> 00:15:37.470 want to see that you can successfully manage 00:15:37.470 --> 00:15:39.850 different kinds of credit. If you only have credit 00:15:39.850 --> 00:15:42.549 cards or you only have a student loan, you haven't 00:15:42.549 --> 00:15:44.929 really demonstrated comprehensive financial maturity. 00:15:45.470 --> 00:15:47.850 So having a mix of revolving debt like credit 00:15:47.850 --> 00:15:50.490 cards and some installment debt like a car loan 00:15:50.490 --> 00:15:52.809 or a mortgage proves you can handle different 00:15:52.809 --> 00:15:54.730 financial structures. It proves you get fixed 00:15:54.730 --> 00:15:56.529 payments, you get interest schedules, you get 00:15:56.529 --> 00:15:59.149 revolving minimums. It just rounds out your profile 00:15:59.149 --> 00:16:01.169 and makes you look like a more reliable borrower. 00:16:01.269 --> 00:16:04.190 Got it. And that brings us to the final 10%, 00:16:04.190 --> 00:16:07.730 the last piece of the FICO formula, and that's 00:16:07.730 --> 00:16:09.809 the search for a new credit, which is mostly 00:16:09.809 --> 00:16:12.409 measured by inquiries. Here's where it gets really 00:16:12.409 --> 00:16:15.870 interesting. Because not all inquiries are created 00:16:15.870 --> 00:16:18.409 equal. Right. An inquiry just means a company 00:16:18.409 --> 00:16:21.330 requested your credit file. But there's a huge 00:16:21.330 --> 00:16:23.690 difference between a soft inquiry and a hard 00:16:23.690 --> 00:16:25.990 one. Let's start with the one that doesn't matter 00:16:25.990 --> 00:16:29.669 for your score. The soft inquiry. Zero effect. 00:16:29.929 --> 00:16:31.929 Zero effect. They're visible to you if you pull 00:16:31.929 --> 00:16:33.870 your own report, and they stay on there for about 00:16:33.870 --> 00:16:36.409 six months. But they are completely invisible 00:16:36.409 --> 00:16:39.309 to potential lenders and to the scoring models. 00:16:39.590 --> 00:16:41.450 So what are some examples of these, the ones 00:16:41.450 --> 00:16:43.230 we shouldn't worry about? Well, there are a few 00:16:43.230 --> 00:16:45.789 big ones. First, those pre -screening inquiries 00:16:45.789 --> 00:16:47.889 for all the pre -approved credit card offers 00:16:47.889 --> 00:16:50.870 you get in the mail. Second, when one of your 00:16:50.870 --> 00:16:53.210 existing creditors does a routine account check 00:16:53.210 --> 00:16:56.710 on you. Third, and this is important, when you 00:16:56.710 --> 00:16:59.669 check your own credit report. So checking your 00:16:59.669 --> 00:17:02.029 own score doesn't hurt your score? Ever. And 00:17:02.029 --> 00:17:04.210 finally, inquiries for things like employment 00:17:04.210 --> 00:17:07.269 screening, getting an insurance quote, or setting 00:17:07.269 --> 00:17:10.190 up utilities. All of those are benign. Now for 00:17:10.190 --> 00:17:12.819 the critical ones. Hard inquiries. These are 00:17:12.819 --> 00:17:14.759 the ones that can actually cost you points. A 00:17:14.759 --> 00:17:18.180 hard inquiry is visible to lenders and to the 00:17:18.180 --> 00:17:20.940 scoring models. And it's triggered when you, 00:17:21.039 --> 00:17:23.660 the consumer, are actively applying for a new 00:17:23.660 --> 00:17:27.259 line of credit or a loan. What's the actual damage 00:17:27.259 --> 00:17:30.079 from one hard inquiry? Is it a 50 -point drop? 00:17:30.480 --> 00:17:33.180 Oh, no, not at all. The impact is generally much 00:17:33.180 --> 00:17:35.619 smaller. A single hard inquiry might cause a 00:17:35.619 --> 00:17:38.000 drop of just a few points, if anything at all, 00:17:38.119 --> 00:17:40.380 and its effect fades pretty quickly, usually 00:17:40.380 --> 00:17:42.519 within a few months, and it falls off your report 00:17:42.519 --> 00:17:45.299 completely after two years. So the real danger 00:17:45.299 --> 00:17:47.660 isn't one inquiry. It's a whole bunch of them 00:17:47.660 --> 00:17:51.640 at once. Exactly. The scoring model sees a cluster 00:17:51.640 --> 00:17:55.019 of hard inquiries in a short period as a sign 00:17:55.019 --> 00:17:57.319 of potential financial desperation. It looks 00:17:57.319 --> 00:18:00.099 like you're scrambling for cash. Right. If you 00:18:00.099 --> 00:18:02.740 apply for five credit cards in one month, the 00:18:02.740 --> 00:18:05.440 model sees that as high risk, and that can cause 00:18:05.440 --> 00:18:08.079 a more noticeable drop in your score. But the 00:18:08.079 --> 00:18:10.119 research points out a very important exception 00:18:10.119 --> 00:18:12.839 to this rule, which is a huge benefit for people 00:18:12.839 --> 00:18:15.539 shopping for big loans. Yes, this is the rate 00:18:15.539 --> 00:18:18.660 shopping safe harbor. The model is smart enough 00:18:18.660 --> 00:18:20.900 to know that when you're buying a car or a house, 00:18:21.059 --> 00:18:23.539 you need to compare rates from multiple lenders. 00:18:23.930 --> 00:18:26.230 So it doesn't penalize you for that. It doesn't. 00:18:26.509 --> 00:18:29.710 FICO models will treat all similar hard inquiries, 00:18:29.750 --> 00:18:32.250 say for a mortgage, that are made within a certain 00:18:32.250 --> 00:18:35.390 time window, usually 14 to 45 days, as if they 00:18:35.390 --> 00:18:37.910 were just a single inquiry. That is such a critical 00:18:37.910 --> 00:18:39.809 piece of information. So you can shop around 00:18:39.809 --> 00:18:41.650 for the best mortgage rate without wrecking your 00:18:41.650 --> 00:18:44.430 score. You can, and you should. Okay, so that 00:18:44.430 --> 00:18:48.829 353 -151010 split is the blueprint. But now that 00:18:48.829 --> 00:18:50.730 we know how the number is calculated, we need 00:18:50.730 --> 00:18:52.789 to get into the practical side of things. How 00:18:52.789 --> 00:18:55.369 do you actually access your report and fix the 00:18:55.369 --> 00:18:57.309 errors we were talking about earlier? And that 00:18:57.309 --> 00:18:59.369 brings us to the legal framework for access, 00:18:59.650 --> 00:19:01.670 which in the U .S. is governed by the Fair Credit 00:19:01.670 --> 00:19:05.069 Reporting Act, the FCRA. The FCRA covers the 00:19:05.069 --> 00:19:08.849 big three we all know, Experian, Equifax, TransUnion, 00:19:08.849 --> 00:19:11.029 but its reach actually extends much further. 00:19:11.089 --> 00:19:12.910 It covers what are called specialty agencies. 00:19:13.349 --> 00:19:15.230 And this is an area that's often overlooked by 00:19:15.230 --> 00:19:18.200 consumers. These specialty agencies cater to 00:19:18.200 --> 00:19:20.680 very specific clients, and they collect very 00:19:20.680 --> 00:19:23.539 specific data. Like what? For example, there 00:19:23.539 --> 00:19:25.539 might be a specialty agency that only collects 00:19:25.539 --> 00:19:28.559 data on payday loan behavior, or one that focuses 00:19:28.559 --> 00:19:31.359 on medical debt, or even one that tracks landlord 00:19:31.359 --> 00:19:33.680 -tenant disputes and eviction records. And why 00:19:33.680 --> 00:19:35.940 do these matter? Because, even though they're 00:19:35.940 --> 00:19:38.599 niche, the FCRA says they have to follow the 00:19:38.599 --> 00:19:41.740 same rules of accuracy and consumer access. And 00:19:41.740 --> 00:19:43.839 that data can still be used against you by... 00:19:44.089 --> 00:19:46.509 say, a landlord who's deciding whether or not 00:19:46.509 --> 00:19:48.710 to rent to you. And the single most important 00:19:48.710 --> 00:19:51.730 requirement the FCRA places on all these agencies, 00:19:51.930 --> 00:19:54.470 big and small. It mandates that they all must 00:19:54.470 --> 00:19:56.809 provide you with a free copy of your own report 00:19:56.809 --> 00:19:59.809 once per year upon request. This isn't a perk. 00:19:59.990 --> 00:20:02.609 It's a federally guaranteed right. It's your 00:20:02.609 --> 00:20:05.650 right to see the data they have on you and check 00:20:05.650 --> 00:20:08.240 it for accuracy. Now let's look globally for 00:20:08.240 --> 00:20:10.420 a second. We notice that Canada, for example, 00:20:10.519 --> 00:20:12.940 approaches this with a really heavy focus on 00:20:12.940 --> 00:20:15.500 education. They do. In Canada, the government 00:20:15.500 --> 00:20:17.799 itself offers this free extensive publication 00:20:17.799 --> 00:20:20.599 called Understanding Your Credit Report and Credit 00:20:20.599 --> 00:20:22.880 Score. So they're trying to empower consumers 00:20:22.880 --> 00:20:26.220 directly. Yes. Explaining the codes, giving strategies 00:20:26.220 --> 00:20:28.480 for building credit, clear guidance on identity 00:20:28.480 --> 00:20:30.579 theft. It's a much more public education focused 00:20:30.579 --> 00:20:32.880 model. And then in Europe, we see some countries 00:20:32.880 --> 00:20:35.119 that integrate their central bank right into 00:20:35.119 --> 00:20:37.359 the system, which is a huge difference from the 00:20:37.359 --> 00:20:39.880 private sector model in the U .S. Right. Take 00:20:39.880 --> 00:20:42.420 Spain. Their central credit register is maintained 00:20:42.420 --> 00:20:44.799 directly by the Bank of Spain. So individuals 00:20:44.799 --> 00:20:47.859 can get their reports for free online or by mail 00:20:47.859 --> 00:20:50.750 from a central government body. Which some would 00:20:50.750 --> 00:20:53.089 argue provides a higher degree of oversight. 00:20:53.410 --> 00:20:55.670 It's certainly a different approach. This global 00:20:55.670 --> 00:20:58.509 view is really illuminating because it highlights 00:20:58.509 --> 00:21:01.569 one of the major systemic flaws in the international 00:21:01.569 --> 00:21:04.430 financial system, the immigrant credit challenge. 00:21:04.730 --> 00:21:08.289 This is such a harsh reality. The research confirms 00:21:08.289 --> 00:21:11.250 that your credit history basically has no international 00:21:11.250 --> 00:21:15.450 portability. An excellent credit rating in, say, 00:21:15.819 --> 00:21:19.019 Germany or Australia means almost nothing when 00:21:19.019 --> 00:21:20.960 you move to the United States. And even if it's 00:21:20.960 --> 00:21:23.640 the same company, Equifax Canada doesn't share 00:21:23.640 --> 00:21:26.480 data with Equifax US. That's the core of the 00:21:26.480 --> 00:21:29.900 problem. An immigrant who has diligently managed 00:21:29.900 --> 00:21:31.980 their credit for 20 years in their home country 00:21:31.980 --> 00:21:34.559 gets here and has to start their financial life 00:21:34.559 --> 00:21:36.980 completely from scratch. They have no local credit 00:21:36.980 --> 00:21:39.519 history. which means an immediate inability to 00:21:39.519 --> 00:21:42.380 access good rates or even get approved for major 00:21:42.380 --> 00:21:44.660 things like a mortgage. It creates these immediate 00:21:44.660 --> 00:21:46.680 practical difficulties. You might have a high 00:21:46.680 --> 00:21:48.859 income so you meet the ability to repay side 00:21:48.859 --> 00:21:51.920 of things, but you have zero data for willingness 00:21:51.920 --> 00:21:54.599 to repay. So you get locked out of standard credit 00:21:54.599 --> 00:21:56.859 cards or reasonable mortgage rates for years. 00:21:57.640 --> 00:22:00.240 The workaround is often things like secured credit 00:22:00.240 --> 00:22:02.480 cards, where you have to deposit your own money 00:22:02.480 --> 00:22:05.559 as collateral. We did find one notable exception 00:22:05.559 --> 00:22:09.220 to this international wall, though. Yes. While 00:22:09.220 --> 00:22:11.500 it's uncommon for lenders to consider international 00:22:11.500 --> 00:22:14.799 history, American Express is specifically noted 00:22:14.799 --> 00:22:18.240 in the sources as one company that can sometimes 00:22:18.240 --> 00:22:20.799 transfer a credit card account from one country 00:22:20.799 --> 00:22:23.259 to another. Which would be a huge lifeline. A 00:22:23.259 --> 00:22:25.839 critical lifeline for establishing local history. 00:22:27.139 --> 00:22:29.759 That's a great strategic hack for anyone moving 00:22:29.759 --> 00:22:32.740 internationally. Now, let's pivot from people 00:22:32.740 --> 00:22:34.220 starting fresh to people who are struggling. 00:22:34.440 --> 00:22:36.720 We need to talk about what's called adverse credit 00:22:36.720 --> 00:22:40.339 history and its significant consequences. Adverse 00:22:40.339 --> 00:22:42.279 credit goes by a lot of different names in the 00:22:42.279 --> 00:22:45.180 industry. You'll hear subprime, non -status, 00:22:45.180 --> 00:22:48.539 impaired, poor, or just bad credit. But they 00:22:48.539 --> 00:22:50.740 all mean the same thing. They all mean a negative 00:22:50.740 --> 00:22:53.119 rating that is considered undesirable by most 00:22:53.119 --> 00:22:56.000 lenders. And it's caused by a concentration of 00:22:56.000 --> 00:22:58.220 those negative items we talked about. Too many 00:22:58.220 --> 00:23:00.339 late payments, collections, public judgments, 00:23:00.539 --> 00:23:02.619 that sort of thing. It's worth remembering why 00:23:02.619 --> 00:23:04.700 the scoring system was even adopted in the first 00:23:04.700 --> 00:23:07.960 place. Before these automated scores, evaluating 00:23:07.960 --> 00:23:12.140 loan applications was a manual, slow, and often 00:23:12.140 --> 00:23:14.720 inconsistent process. And the sources say the 00:23:14.720 --> 00:23:17.420 shift to scoring brought two huge benefits. Right. 00:23:17.960 --> 00:23:20.319 First, it made credit available to more people. 00:23:20.670 --> 00:23:23.049 And second, it lowered the cost of processing 00:23:23.049 --> 00:23:26.710 applications. It was a standardized, rapid, and 00:23:26.710 --> 00:23:30.190 at least in theory, a without prejudice way to 00:23:30.190 --> 00:23:32.430 assess risk. Because at its heart, the score 00:23:32.430 --> 00:23:35.490 is just a risk gauge. Exactly. It's just comparing 00:23:35.490 --> 00:23:37.829 your behavior to the behavior of millions of 00:23:37.829 --> 00:23:40.069 other debtors to predict how likely you are to 00:23:40.069 --> 00:23:42.569 default. When your history triggers that low 00:23:42.569 --> 00:23:44.910 score, that's what the financial world calls 00:23:44.910 --> 00:23:46.930 adverse credit. The most obvious consequence 00:23:46.930 --> 00:23:49.230 is a huge drop in your chances of getting approved 00:23:49.230 --> 00:23:52.170 for a loan. And if you are approved, the terms 00:23:52.170 --> 00:23:54.589 are often financially punishing. It goes right 00:23:54.589 --> 00:23:56.349 back to that risk -based pricing we discussed. 00:23:56.670 --> 00:23:59.130 An adverse credit history directly leads to a 00:23:59.130 --> 00:24:01.509 significantly higher interest rate. Because the 00:24:01.509 --> 00:24:04.170 lender is hedging their bets. The extra interest 00:24:04.170 --> 00:24:06.769 you pay is designed to offset the higher statistical 00:24:06.769 --> 00:24:08.970 default rate for people in your score range. 00:24:09.250 --> 00:24:12.140 Precisely. But the consequences, especially in 00:24:12.140 --> 00:24:15.579 the U .S., go way beyond just loans. The FCRA 00:24:15.579 --> 00:24:18.220 allows entities with a permissible purpose to 00:24:18.220 --> 00:24:20.599 see your information. And this is a critical 00:24:20.599 --> 00:24:23.339 point for our listeners. Denial or bad terms 00:24:23.339 --> 00:24:26.259 can extend to major life necessities, insurance, 00:24:26.700 --> 00:24:29.779 housing, and employment. Let's start with employment 00:24:29.779 --> 00:24:31.740 because that's the one that often surprises people. 00:24:32.269 --> 00:24:34.910 A 2013 survey showed that employer credit checks 00:24:34.910 --> 00:24:36.789 were actively keeping people from getting back 00:24:36.789 --> 00:24:39.309 into the workforce. What did it find? It found 00:24:39.309 --> 00:24:42.009 that one in four unemployed Americans was required 00:24:42.009 --> 00:24:44.589 to undergo a credit check when applying for a 00:24:44.589 --> 00:24:47.029 job. And why would an employer care about your 00:24:47.029 --> 00:24:49.750 credit? For certain roles, especially in finance 00:24:49.750 --> 00:24:52.390 or jobs with access to sensitive data, a poor 00:24:52.390 --> 00:24:54.329 credit history is seen as a sign of potential 00:24:54.329 --> 00:24:57.650 irresponsibility, or worse, a vulnerability to 00:24:57.650 --> 00:25:00.519 things like fraud or bribery. No, there are regulations. 00:25:01.140 --> 00:25:02.819 Employers have to get your written permission 00:25:02.819 --> 00:25:05.119 first. But it's hard to prove you were denied 00:25:05.119 --> 00:25:07.380 a job because of your credit history. It's a 00:25:07.380 --> 00:25:10.359 very opaque system. And it creates this huge 00:25:10.359 --> 00:25:12.720 hurdle for people trying to recover financially. 00:25:13.339 --> 00:25:15.240 It's worth noting, though, that some states, 00:25:15.279 --> 00:25:17.980 like California and Illinois, have passed laws 00:25:17.980 --> 00:25:20.500 restricting the use of credit checks for most 00:25:20.500 --> 00:25:23.700 jobs. What about housing? Can a landlord deny 00:25:23.700 --> 00:25:26.180 you an apartment because of a low score? Absolutely. 00:25:26.980 --> 00:25:29.440 Landlords use credit reports and scores all the 00:25:29.440 --> 00:25:31.400 time to predict the likelihood of you paying 00:25:31.400 --> 00:25:34.220 your rent on time. A high score suggests you're 00:25:34.220 --> 00:25:36.680 reliable. A low score with a recent history of 00:25:36.680 --> 00:25:39.160 collections or non -payment suggests you're a 00:25:39.160 --> 00:25:42.220 high risk, which can lead to a denial or a demand 00:25:42.220 --> 00:25:44.579 for a much higher security deposit. Before we 00:25:44.579 --> 00:25:46.519 move on, let's just clarify who makes the final 00:25:46.519 --> 00:25:49.319 call here. The credit bureaus don't decide if 00:25:49.319 --> 00:25:51.259 your history is adverse. That's a key point. 00:25:51.579 --> 00:25:54.500 That decision rests entirely with the individual 00:25:54.500 --> 00:25:57.440 lender or creditor. They use their own internal 00:25:57.440 --> 00:25:59.740 proprietary guidelines. Which are usually secret. 00:25:59.920 --> 00:26:02.359 For competitive reasons, yes. So a score that's 00:26:02.359 --> 00:26:05.240 perfectly fine for lender A might be too low 00:26:05.240 --> 00:26:07.920 for the risk profile of lender B. But if they 00:26:07.920 --> 00:26:10.619 deny you credit, they can't just say no. No. 00:26:10.740 --> 00:26:13.240 In the U .S., the creditor has to immediately 00:26:13.240 --> 00:26:15.380 give you the specific reasons for the denial. 00:26:16.160 --> 00:26:18.299 And they have to provide the name and address 00:26:18.299 --> 00:26:20.680 of the credit reporting agency whose data they 00:26:20.680 --> 00:26:23.940 used. Which lets you circle back, get your free 00:26:23.940 --> 00:26:26.339 report, and dispute the exact information that 00:26:26.339 --> 00:26:28.559 was used against you. It closes the loop. Okay. 00:26:28.599 --> 00:26:31.140 We have spent a lot of time on how to manage 00:26:31.140 --> 00:26:33.880 this system properly. Now we have to look at 00:26:33.880 --> 00:26:37.880 the dark side. How this incredibly complex, data 00:26:37.880 --> 00:26:40.839 -heavy system gets exploited, both from the outside, 00:26:41.160 --> 00:26:44.079 by criminals, and from the inside, by the powerful 00:26:44.079 --> 00:26:46.619 agencies themselves. You know, the system is 00:26:46.619 --> 00:26:48.660 designed to create trust, but because all this 00:26:48.660 --> 00:26:51.559 valuable data is so centralized, it's inherently 00:26:51.559 --> 00:26:53.819 vulnerable. And the research points to several 00:26:53.819 --> 00:26:55.900 well -known ways people exploit those vulnerabilities. 00:26:56.440 --> 00:26:59.079 Let's start with the external abuses. One technique 00:26:59.079 --> 00:27:01.900 the sources cite is churning. Churning is basically 00:27:01.900 --> 00:27:04.039 exploiting new credit card offers over and over 00:27:04.039 --> 00:27:06.019 again. People will open accounts just to get 00:27:06.019 --> 00:27:09.539 huge sign -up bonuses or 0 % APR periods, and 00:27:09.539 --> 00:27:11.599 then they'll close them or stop using them almost 00:27:11.599 --> 00:27:14.000 immediately. It's an industrialized way to maximize 00:27:14.000 --> 00:27:16.259 rewards. Then there's something called rapid 00:27:16.259 --> 00:27:19.259 -fire credit applications. This one is riskier. 00:27:19.720 --> 00:27:21.779 It's where someone applies for a whole bunch 00:27:21.779 --> 00:27:24.640 of loans or credit cards in a very short amount 00:27:24.640 --> 00:27:27.519 of time, gambling that they can get approved 00:27:27.519 --> 00:27:30.200 for a lot of credit before all those hard inquiries 00:27:30.200 --> 00:27:32.819 hit the bureaus and their score plummets. The 00:27:32.819 --> 00:27:34.819 sources also talk about piggybacking. What's 00:27:34.819 --> 00:27:37.920 that? Piggybacking is using authorized user status 00:27:37.920 --> 00:27:41.440 on someone else's good credit account. Historically, 00:27:41.480 --> 00:27:42.900 you know, parents would do this to help their 00:27:42.900 --> 00:27:45.579 kids build credit. But now it's been commercialized. 00:27:45.640 --> 00:27:48.319 It's become a black market. People with excellent 00:27:48.319 --> 00:27:51.720 long credit histories will sell access to their 00:27:51.720 --> 00:27:54.279 accounts. They call them trade lines to people 00:27:54.279 --> 00:27:56.319 who want an instant score boost by association. 00:27:56.619 --> 00:27:59.079 And then there's the most insidious external 00:27:59.079 --> 00:28:02.359 abuse and fabricated files. This is straight 00:28:02.359 --> 00:28:05.079 up identity fraud. Criminals have found ways 00:28:05.079 --> 00:28:07.819 to exploit regulatory blind spots to create entirely 00:28:07.819 --> 00:28:10.359 fake credit files within the system. They build 00:28:10.359 --> 00:28:12.859 up these fake identities, get high credit limits 00:28:12.859 --> 00:28:15.279 and then take out huge loans they never intend 00:28:15.279 --> 00:28:17.640 to repay. And the biggest vulnerability of all 00:28:17.640 --> 00:28:20.440 is just the sheer concentration of data, which 00:28:20.440 --> 00:28:23.059 was laid bare by those massive data breaches. 00:28:23.079 --> 00:28:26.390 The Equifax breaches in 2017 were. They were 00:28:26.390 --> 00:28:29.130 seismic events. They just exposed the incredible 00:28:29.130 --> 00:28:31.910 risk of having all this sensitive consumer data 00:28:31.910 --> 00:28:34.950 names, addresses, social security numbers centralized 00:28:34.950 --> 00:28:37.670 in just a few private companies. It proved that 00:28:37.670 --> 00:28:40.150 the gatekeepers themselves were prime targets. 00:28:40.450 --> 00:28:42.509 Absolutely. But the fraud doesn't just come from 00:28:42.509 --> 00:28:45.029 the outside. The sources we looked at highlight 00:28:45.029 --> 00:28:47.230 that fraud can also be committed on consumers 00:28:47.230 --> 00:28:49.230 by the credit reporting agencies themselves. 00:28:49.569 --> 00:28:52.049 And that just destroys consumer trust. It completely 00:28:52.049 --> 00:28:55.180 breaks the model of an unbiased system. We have 00:28:55.180 --> 00:28:58.460 a powerful documented example of this from 2017. 00:28:58.920 --> 00:29:01.420 It involves huge fines from the Consumer Financial 00:29:01.420 --> 00:29:04.299 Protection Bureau, the CFPB. Right. Equifax and 00:29:04.299 --> 00:29:07.680 TransUnion were fined a combined $23 .3 million 00:29:07.680 --> 00:29:10.480 for deceiving customers about the cost of their 00:29:10.480 --> 00:29:12.619 own services. How are they deceiving people? 00:29:12.900 --> 00:29:14.900 They were caught selling credit monitoring services 00:29:14.900 --> 00:29:18.119 directly to consumers. These services were advertised 00:29:18.119 --> 00:29:20.480 at a really low price, like a dollar, to get 00:29:20.480 --> 00:29:22.380 people to sign up. But that wasn't the real price. 00:29:22.740 --> 00:29:25.849 No. Consumers were unknowingly being enrolled 00:29:25.849 --> 00:29:27.950 in these expensive auto -renewing subscriptions, 00:29:28.450 --> 00:29:31.750 sometimes getting billed to $200 a year. It was 00:29:31.750 --> 00:29:34.230 a clear -cut case of intentional deception. They 00:29:34.230 --> 00:29:37.009 were exploiting consumer anxiety about the very 00:29:37.009 --> 00:29:39.309 data they're supposed to be protecting. Exactly. 00:29:39.410 --> 00:29:42.930 The agencies tasked with being the unbiased arbiters 00:29:42.930 --> 00:29:45.670 of your financial life were actively acting as 00:29:45.670 --> 00:29:48.640 predators for short -term profit. That is deeply 00:29:48.640 --> 00:29:51.099 troubling. This has been an incredibly detailed 00:29:51.099 --> 00:29:53.660 deep dive. It's probably time to summarize the 00:29:53.660 --> 00:29:55.480 essential takeaways for the listener. I think 00:29:55.480 --> 00:29:59.099 so. First, the foundational difference. The credit 00:29:59.099 --> 00:30:01.440 report is your history, the document of your 00:30:01.440 --> 00:30:04.140 past. The credit score is the mathematical prediction 00:30:04.140 --> 00:30:07.039 of your future. Never confuse the two. And strategically, 00:30:07.299 --> 00:30:09.700 if you want to maximize that score, the FICO 00:30:09.700 --> 00:30:12.519 formula is your map. Payment history at 35 % 00:30:12.519 --> 00:30:15.720 and debt utilization at 30 % are the kings. Keep 00:30:15.720 --> 00:30:18.240 that utilization below 10 % if you can and just 00:30:18.240 --> 00:30:20.299 never, ever miss a payment. That's more than 00:30:20.299 --> 00:30:22.940 half the game right there. And we also emphasized 00:30:22.940 --> 00:30:26.500 your legal rights. The FCRA guarantees you a 00:30:26.500 --> 00:30:29.519 free annual credit report and it legally mandates 00:30:29.519 --> 00:30:33.000 that 30 -day process for disputing errors. Knowing 00:30:33.000 --> 00:30:36.319 those rights is your best defense. It is. So 00:30:36.319 --> 00:30:38.359 what does this all mean? I mean, understanding 00:30:38.359 --> 00:30:41.109 the system is just fundamental. This three -digit 00:30:41.109 --> 00:30:43.849 number dictates the cost of living and your access 00:30:43.849 --> 00:30:46.650 to major life opportunities, housing, insurance, 00:30:47.069 --> 00:30:49.690 employment. It really does. It determines whether 00:30:49.690 --> 00:30:51.849 you get the standard rate or the rate that's 00:30:51.849 --> 00:30:54.569 designed to offset your risk of default. This 00:30:54.569 --> 00:30:56.569 knowledge is what empowers you to control that 00:30:56.569 --> 00:30:58.609 access. And this brings us to our final thought, 00:30:58.750 --> 00:31:00.769 something for you to mull over after everything 00:31:00.769 --> 00:31:02.930 we just discussed. Okay, so credit scores were 00:31:02.930 --> 00:31:04.650 originally adopted with this promise of being 00:31:04.650 --> 00:31:07.869 a rapid, standardized, and theoretically an unbiased 00:31:07.869 --> 00:31:11.039 assessment of risk. A system without prejudice. 00:31:11.299 --> 00:31:14.119 But we've documented all these significant issues, 00:31:14.380 --> 00:31:17.299 lingering data inaccuracy, the system's vulnerability 00:31:17.299 --> 00:31:20.220 to fraud, and critically intentional deception 00:31:20.220 --> 00:31:22.819 and abuse by the credit bureaus themselves, the 00:31:22.819 --> 00:31:25.279 ones who run the system. Which led to multi -million 00:31:25.279 --> 00:31:27.859 dollar fines. Right. So given these documented 00:31:27.859 --> 00:31:31.039 failures and biases, to what extent can a purely 00:31:31.039 --> 00:31:33.940 mathematical algorithm truly assess something 00:31:33.940 --> 00:31:37.299 as human as willingness to repay a debt? And 00:31:37.299 --> 00:31:39.579 how does the current system uphold that original 00:31:39.579 --> 00:31:42.980 promise of being without prejudice when the gatekeepers 00:31:42.980 --> 00:31:45.299 themselves have been proven to act as predators? 00:31:45.619 --> 00:31:48.460 That is the question worth mulling over. A powerful 00:31:48.460 --> 00:31:50.339 thought. Thanks for diving deep with us. We'll 00:31:50.339 --> 00:31:50.839 see you next time.