The AI In Fintech Market Map

OK – so how cool is this? James being included in the just released Top 100 AI fintech firms, as per the boys and gals at CB Insights. Thanks guys, so top 10 is my next target now. 

Funding to AI startups reached record highs in 2016 and applications for artificial intelligence technologies exist across nearly the entire spectrum of business. Highlighted here are the top 100 AI startups selected by CB Insights operating across numerous industry verticals.

Using the CB Insights database, we expanded upon our AI 100 analysis to identify companies that use AI in financial services and mapped them according to the areas where they’re operating. In broad terms, our analysis includes companies whose core offering includes the application of AI to serve the financial services industry, including commercial banking and credit offerings, insurance, asset management, accounting & personal finance, as well as regulatory & compliance services. In addition, many of these companies have additional use cases beyond financial services.

Source: The AI In Fintech Market Map

Credit Reports to Exclude Certain Negative Information, Boosting FICO Scores.

OK – not sure about this, but it has a distinct “deja-vu” feeling in my book – and if one has been in this business as long as I have, it’s clearly not pointing to anything good here.

The decision by the three major credit-reporting firms— Equifax Inc., Experian PLC and TransUnion—could help boost credit scores for millions of U.S. consumers, but could pose risks for lenders. The reports and scores often help decide how much consumers can borrow for a new house or car as well as determine their credit-card spending limit.

The unusual move by the influential firms comes partially in response to regulatory concerns. The three reporting bureaus rarely tinker with the information that goes on credit reports and that lenders consult to gauge consumers’ ability and willingness to pay back debts.

So let me get this straight. We’re going to make it easier for people to access credit (a good thing) by artificially boosting their credit rating (a bad thing)? Reminds me of the pre-housing bust period, where everybody and his dog got approved for NINJA loans, in the name of “democratization of access to housing”, and we’ve all seen what that has brought us. We’re still digging out of that one.

Maybe useful to read the latest Orchard stats on marketplace lending charge-offs by quarter as per the article here:

Source: Credit Reports to Exclude Certain Negative Information, Boosting FICO Scores – WSJ

Is it OK for lending algorithms to favor Ivy League schools?

This article goes deeper into the subject matter, and addresses a number of issues that are important indeed. In the long run though, it’s clear that this has only one way to go, and it’s a positive story. The human factor being what it is, there is always going to be a bias. Put more machines in charge, let them learn, and we’re off to a better credit world, no doubt. 

The main mantra used to be “software is eating the world”. Mine these days and going forward is “AI and Machine Learning is eating all the rest”. TGIF – but have a good read nevertheless.

“I think a baseline question is, how much disparate impact already exists in the system?” said Paul Gu, co-founder of the online consumer lender Upstart, which includes the potential borrower’s college in its underwriting criteria. “I think we would be kidding ourselves if we thought that the traditional way of underwriting was a completely unbiased way of underwriting. If you look at credit scores by any demographic, they’re extremely uneven. If you look at credit access in America, it’s extremely uneven.”

Source: Is it OK for lending algorithms to favor Ivy League schools?

I’m Renting a Dog? – Bloomberg

Oh my … really not sure what to think here. Technology to the rescue? Canine overreach? Smells like a bad combination of otherwise good intentions. But again, where’s the moral compass? Am I alone in thinking this is not the right way to do things? Up next I guess, … renting your wife and kids – that’ll be a new low.

In Wunderlich’s telling, U.S. lenders do a good job of pricing credit for prime borrowers, lowering their interest rates as their credit scores rise. But lenders have taken a cruder approach with the millions of subprime borrowers, extending the same high interest rates to large swaths, regardless of their individual credit histories. Wunderlich says he wants to “democratize access to credit through dynamic pricing across the credit spectrum”—a fancy way of saying his customers pay rates based on their own ability to repay, not someone else’s.

Source: I’m Renting a Dog? – Bloomberg

How fintechs are using AI to transform payday lending

Big Data, AI and Machine Learning getting some needed attention here, no less by the people at American Banker. Something’s afoot? Yes indeed, and we ain’t seen nothing yet. The upward march will be long and “volatile”, but has only one way to go. It’s one of the great ways we’ll be able to help a large part of the un- and underserved population at many different a level. Stay tuned for more, but enjoy the observations below.

“Flannery said machine learning engines are less discriminatory than people.”

“Humans tend to do things like redlining, which is completely ignoring an entire class,” he said. “Machine learning algorithms do [lending] in a multidimensional, ‘rational’ way.”

penny.crosman@sourcemedia.com

Source: How fintechs are using AI to transform payday lending

How banks work with thinking machines | FT Alphaville

In this FT column, a good case for “intelligence” made by the COO of UBS, with things like: “…As machines become more “intelligent”, they will be able to undertake more complex tasks, like credit scoring or automated report writing. For example, we are already using a leading cognitive computing solution to handle IT incidents more efficiently.” Clearly, with credit scoring we’re already all-in, with further progress anything but assured. 

As machines become more “intelligent”, they will be able to undertake more complex tasks, like credit scoring or automated report writing. For example, we are already using a leading cognitive computing solution to handle IT incidents more efficiently.

Source: How banks work with thinking machines | FT Alphaville