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?

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.”

Source: How fintechs are using AI to transform payday lending

Why branch bankers shouldn’t fear bots

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.

Source: Why branch bankers shouldn’t fear bots

CB Insights on Twitter: “Artificial intelligence has jumped the shark”

No further comment needed #CrowdProcess #James #Marketplacelendingrevolution

Management online lending risk with AI & CrowdProcess / James

Most timely update from the team at Lending Times on CrowdProcess, its flagship product “James” and its plans for US domination … guess who’s gonna lead that charge? 

James’ features include:

  • Scorecard: Traditional scorecards have been augmented with a random decision factor, gradient boosting, and neural networks.
  • Validate: It provides precautionary alerts according to the model`s metrics and access to data necessary for validation. James has an edge over other solutions in the market, which may not enable regulators to validate.
  • Deployment: James has superiority with regards to the timeline of deployment. The model can be run automatically or in conjunction with other models. So a 6-month integration time is essentially reduced to zero and the company can compare multiple models in parallel.
  • Monitoring: James monitors its own performance in real-time. The system not gives out early warnings and helps ensure data sets are used appropriately. When a material change occurs, the software is tweaked accordingly.

Source: Management online lending risk with AI | Lending Times

Fintech 2017 – Breaking Banks

Good to see CrowdProcess/James being featured here as well – with the following details:

“There have been big advancements in AI over just a short number years owing to better algorithms, much larger datasets for training them, cheaper and faster cloud-based infrastructure and new computing techniques like parallel computing. On the back of this, we are seeing many more AI-related fintechs entering the jams, especially compared to two years ago.

An interesting example from the UK is Crowdprocess whose product James leverages machine learning algorithms to create high-performing predictive models and scorecards for credit risk management.”

Source: Fintech 2017 – Breaking Banks

Equifax And SAS Leverage AI And Deep Learning To Improve Consumer Access To Credit

Great to see some of the players getting more involved in what is probably the most exciting sub space in credit – and we ain’t seen nothing yet. The writing is on the wall, and further expect some of the category killers to grow more monstrous as take up by competitors is (too) slow. Peter Thiel’s “Zero to One” comes to mind, as I continue to ponder … FB and Google combined controlling north of 85% of the online ad market … Amazon controlling 40%+ of the US e-commerce market … Size does matter, over and over again. 

The result—and the most important benefit of using modern machine learning tools—is greater access to credit. Analyzing two years’ worth of U.S. mortgage data, Equifax determined that numerous declined loans could have been loaned safely. That promises a considerable expansion of the universe of approved mortgages. “The use case we showed regulators,” says Maynard, “was in the telecom industry where people had to put down a down payment to get a cell phone—with this model they don’t need to do that anymore.”

Source: Equifax And SAS Leverage AI And Deep Learning To Improve Consumer Access To Credit