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On the Rise of FinTechs – Credit Scoring Using Digital Footprints
- yjftsjthsd-h 8y ago"We analyze the information content of the digital footprint – information that people leave online simply by accessing or registering on a website – for predicting consumer default." Wonderful. Gameable and dystopian all at the same time.
- Spartan-S63 8y agoFeels like the open market is coming up with a solution similar to China's "social credit" scoring system.
- azborder 8y agoThe only thing stopping it in the US is the equal credit act. Most of these “novel” credit scoring solutions are just attempts to work around the race and other prohibitions in credit scoring. The good news is that these things get shut down quickly with enough complaints. This study points out that the digital tracking is likely a violation.
- ThrowAway1451 8y agoIf that is true ("equal credit act is the only thing stopping it in the US"), then race is the strongest factor[1] for credit scoring, so I'm not sure how China does it (Chinese population is racially homogenous)? [1] https://en.wikipedia.org/wiki/Factor_analysis https://en.wikipedia.org/wiki/Factor_analysis
- voidmain0001 8y agoThe Chinese recognize race within its borders[1], additionally it may be of interest to the scorer to know who is Hui, Tibetan, or Uyghur.[2]. I don't support this I'm just noting that China is not racially homogenous. [1] https://en.m.wikipedia.org/wiki/Five_Races_Under_One_Union https://en.m.wikipedia.org/wiki/Five_Races_Under_One_Union [2] https://en.m.wikipedia.org/wiki/Ethnic_issues_in_China https://en.m.wikipedia.org/wiki/Ethnic_issues_in_China
- Rjevski 8y agoAnd this is where I’m grateful for GDPR to exist.
- adrr 8y agoProblem with utilizing datapoints like digital footprints is that it will run afoul equal credit opportunity act. ECOA was designed to stop banks from redlining neighbourhoods which usually punished minorities. With digital footprints, they'll be in theory redlining their digital including sites, products purchased etc.
- 21 8y agoThere was another article recently which argued that because there is a strong correlation between race and default rates, if you apply machine learning to a dataset, the algorithm will find a way to extract what is basically a proxy for the race from the data. So basically any sort of ML applied to credit data will run afoul of the equal credit opportunity act. The article also made the point that basically all ML credit scoring startups are illegal because of this, but they get away just because they are small and not on the radar.
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- zt 8y agoI would love to know what that article is! You have any more info? I made parts of this argument recently: https://www.americanbanker.com/opinion/regulators-need-to-step-up-their-game-to-stop-tech-based-discrimination https://www.americanbanker.com/opinion/regulators-need-to-st...
- 21 8y agohttps://thenextweb.com/syndication/2017/12/29/future-fintech-racist-according-anonymous-data-scientist/ https://thenextweb.com/syndication/2017/12/29/future-fintech... I do remember it being longer, they mention this is an except, maybe I've read the original which I can't find now.
- danieltillett 8y agoIs it race or culture? Do you know if recent immigrants show the same "race" signal or not. I wonder how much race signal remains after accounting for g. I know in a lot of other areas once you correct for g there is almost no race signal left.
- ubernostrum 8y agoSo. During the days of officially/semi-officially-sanctioned racial discrimination in the US, it didn't really matter whether you were a dark-skinned person who was born in the US, or a dark-skinned person born in the Caribbean who immigrated here, or a dark-skinned person born in Africa who immigrated here. The only thing that system cared about was your perceived race, defined primarily by your skin color, and so every dark-skinned person got subjected to discrimination. With recent immigrants today you can see some differences, but it's not due to immigrants having different "culture"; it's due to the historical baggage of the long, long period of discrimination suffered by folks who were already here. The immigrant probably has the benefit, for example, of an extended family that worked hard to save up and send someone to the US and provide advice and support and broker connections; the US-born dark-skinned person has had their extended family deliberately broken up, and subjected to policies that prevent intergenerational accumulation of wealth or other resources. And that's not the only sort of head start the immigrant gets, which means it should be totally unsurprising if we now see better outcomes on average for recent immigrants. And now all the assumptions and stereotypes based on perceived race are being used as training data for "objective" ML/AI systems whose creators promise they're free of prejudice...
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- incompatible 8y agoThe attributes, from the paper: The device type (for example, tablet or mobile), the operating system (for example, iOS or Android), the channel through which a customer comes to the website (for example, search engine or price comparison site), a do not track dummy equal to one if a customer uses settings that do not allow tracking device, operating system and channel information, the time of day of the purchase (for example, morning, afternoon, evening, or night), the email service provider (for example, gmail or yahoo), two pieces of information about the email address chosen by the user (includes first and/or last name and includes a number), a lower case dummy if a user consistently uses lower case when writing, and a dummy for a typing error when entering the email address.
- incompatible 8y agoA lot of potential for injustice via false negative judgements; after all, you aren't doing anything wrong by using a particular OS at a particular time of day, etc.
- trendia 8y agoI love that "Do not track" is not interpreted as an instruction to not track but rather as an additional 1 bit of discriminatory data.
- acjohnson55 8y agoI feel like none of these things should be considered relevant to access to credit.
- taurine 8y agoLet's play a game. You are in charge of a large pile of cash and want to make it grow by giving loans. Each day, two people apply, and you can give out one loan (you will have to rank the applicants). When people de-fraud you, you lose all of the loan. When people don't or can't pay you back, you lose all of the loan. When people pay back the loan, you make a little money. Day1: User Agent: iPhone latest vs. Windows XP Day2: Referral: Facebook friend vs. search "cheapest loans" Day3: Time of interaction: 21:30 vs. 04:30 Day4: Email: ari.johnson@cs.mit.edu vs. hpqwoovz11721@hotmail.com Day5: Funnel: Someone who spend 10 seconds vs. someone who spend 10 minutes, made a mistake in the name, entered an email address, then deleted it, and entered another email address at a different provider. Now if your feeling does not point you to the first applicant every day, you look at the data for guidance. You find that the number of fraudsters and non-pay's is statistically significantly higher for people with the second set of characteristics. The alternative is to use third-party data providers. That's another can of worms. Or flip a coin and start gambling proper.
- rahimnathwani 8y agoI lead the Data Science team at Oakam, a London-based fintech company founded in 2006. If you find the article interesting, you may also be interested in Oakam's work using alternative data to predict credit default, which was covered recently in The Economist: https://www.economist.com/special-report/2018/05/03/mobile-financial-services-are-cornering-the-market https://www.economist.com/special-report/2018/05/03/mobile-f... https://www.economist.com/special-report/2018/05/03/mobile-financial-services-are-cornering-the-market https://www.economist.com/special-report/2018/05/03/mobile-f... If you're a Data Scientist looking to work in this area, or just looking for a new challenge, please contact me (personal email in my profile) so we can have a chat! We are also hiring software engineers (stack is React Native for iOS/Android, and mostly C# for everything else).
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- acjohnson55 8y agoThe book Weapons of Math Destruction talks all about this. I've come to believe that pure risk shouldn't be the only factor in a person's interest rate. That obviously makes a ton of sense from a business standpoint. You want to contain losses for risky borrowers but compete with other lenders for low risk borrowers. But socially, this is perverse. People tend to be risky because they are already poor. So now money costs more for those who have the least of it. This is one of the feedback loops that makes poverty (and affluence, for that matter) so sticky. I had this realization in my personal experience when I was able to refinance almost $100k in student loans at a crazy low interest rate. My household's finances are in great shape as my wife and I enter our prime earning years. But for us, such an opportunity is a gift, on top of an already sweet situation. The savings could be a game changer for a family whose finances are more marginal.
- tcbawo 8y agoThat's the irony of credit, plenty of it when you don't need it. It seems that the more money you have, the quality of investments that people approach you with increases too.
- ghthor 8y agoThat's why pretty much the lowest common denominator is fixing the poverty problem manifests as education. Societies that make education one of there main investments will continue to reduce crime rates, increase social safety nets and second chances, and I hypothesize these countries will have most stable positive growth rates in there economies.
- bradleyjg 8y agoHow do you operationalize that? If you are in charge of investments for a teachers’ pension fund, do you invest in less profitable banks because they have looser underwriting standards?
- taurine 8y agoYou can overdo fairness and cause troubles to the poor. Very concretely: Giving someone a loan, despite their credit score being marginal, will severely mess up their credit score forever, if they can't repay you. If you are poor, then don't create more debt! It should be hard to rack up such a debt, not easy and accessible. No amount of credit is going to increase your social status, because you have to pay it back with your own current/near-future money. If we want social justice for the poor through access to more money, then capitalism is not a good way to go. The state should become a credit provider.
- pps43 8y agoThe baseline (FICO) has AUC of 68.3% which looks low. This may be because the analysis is performed not on the entire through-the-door population, but only on the customers that passed the creditworthiness check (which is using FICO). In such situations it is customary to do some kind of reject inference or testing below the cutoff, as well as swap-in and swap-out analysis. It does not look like they did any of that.