Ain’t seen nothing yet …
Ain’t seen nothing yet …
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:
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.”
You want to hear from the big kahuna – you got it!
All you wanted to know about Big Data, AI and Machine Learning, applied also to the un- and underbanked part of the population, call it broadening of access to credit.
“Ash Gupta is an industry leader in machine learning and big data analytics. He promotes AXP wide innovation to drive revenue growth along with best-in-class Credit and Fraud results. His responsibilities extend across all AXP businesses and geographies. He is an executive officer and reports to the company’s Chairman & CEO. Mr. Gupta’s prior roles include company’s Chief Risk officer and CEO of US banking, along with broad leadership positions in Finance and Strategic Planning. Mr. Gupta earned an MBA from Columbia University and a bachelor’s in Engineering from Indian Institute of Technology (IIT), Delhi. Currently, he serves on the boards of Encore Capital Group (NASDAQ: ECPG), Big Brothers Big Sisters of New York, and South Asian Youth Action (Advisory Board).”
John Sculley is the Vice Chairman to Lantern Credit, where he joined the Board of Managers in August 2015. Mr. Sculley served as chairman, CEO and CTO during a decade-long career at Apple Computer, Inc., following his tenure as the youngest president of the Pepsi-Cola Company. Most recently, he has founded several companies including Obi Worldphone, a Silicon Valley design-led company that markets high-quality smartphones at affordable price points. Additionally, Mr. Sculley is a founder of Zeta Interactive, one of the largest marketing cloud firms in consumer marketing-tech. He currently is an author, recognized expert and popular speaker about high-tech tools for tackling challenges such as corporate revitalization and the high cost of healthcare. Mr. Sculley received a bachelor’s degree from Brown University and an MBA from the Wharton School of the University of Pennsylvania.
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
There is a reason why they are slowly but surely going to crush it. Plain vanilla approach, no IVR, no fees, no nothing to p… you off. Sounds like a no-brainer to me, though it shouldn’t be. No wonder most of the players can’t make a profit, and that goal will remain elusive for most of them for the foreseeable future. Not for these guys though. Go Marcus!
Goldman Sachs, a recent entrant to the field, is no exception. At the LendIt conference in New York Tuesday, Harit Talwar, head of digital finance at the investment bank, explained this and other components of Goldman’s strategy for its four-month-old online lending division, Marcus.
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.”
I don’t disagree with this observation, but wanted to add a couple of thoughts here. For one, I strongly believe that over time, we’ll see a re-evaluation of the importance of people vs. machines, as we’ll try to deal with some major headwinds and other unpleasant side effects of current developments re. automation.
More importantly, what I miss in this particular piece, is the fact that AI & Machine Learning is not only there to replace repetitive and low value add activities. It is also there to grow the business, in this case (FI’s) by pointing to opportunities to better serve the un- and underbanked population, who currently are falling through the (credit box) cracks.
Much of the talk about artificial intelligence in banking has been about how technology can replace some functions currently performed by humans. But AI could help human bankers do their jobs more effectively by giving them quicker access to relevant information than ever before.