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Solving the Challenge of Increasing Financial Inclusion with New Models

 

ALIYA

On the second day of the Strum Executive Innovation Summit, attendees heard from S. P. "Wije" Wijegoonaratna, entrepreneur & macro investor, and founder of Alyia.

A data-driven solutions company, Aliya, seeks to improve the lives of those ignored by the banking system. They have focused efforts on helping solve the problem of financial inclusion in the U.S. by developing data driven tools – machine learning risk segmentation algorithms that successfully finds Prime risk borrowers from within the non-Prime customer base.

Wije quickly brought the audience up to speed by framing the situation in understandable terms.

The U.S. consistently ranks as one of the richest countries in the world, yet these statistics obscure the facts indicating the financial health of its population. One such statistic is that over half of the adult population in U.S. have a FICO score below 720 and therefore struggle to gain access to credit or if they do, it is not at a fair price.

Wije talked passionately on the need to use new models to address the massive problem of financial inclusion in the U.S. He argued that it is the social mandate of all financial institutions to take in hard earned deposits from customers/members and lend to those that can benefit from the liquidity, in a fair and responsible manner.

The problem is that the US has experienced a recession in every decade going all the way back to 1980s. And, like clock work, every single time, the financial system has managed to lose money and go into a tailspin during these recessionary times. This begs the question why does it happen over and over again? Surely one would learn from one’s mistakes, and prevent repetition. What’s the impact on the population?

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Take what the financial system did immediately after the Great Recession, the prime and lower hardworking Americans were asked to repay their loans while the Prime Plus and Super Prime customers were able to borrow more. Those most in need of liquidity had to find alternative sources of cash so that they could manage their day to day lives.

Over the last 10 years, the online/alternative lending space has originated close to $175B (Transunion). The problem is that the interest rate that these lenders charge is at least twice the rate a borrower would get at a bank if their credit risk were to be properly assessed and managed.

“Using modern machine learning techniques that are proven, to help these folks, we at Aliya have focused our efforts on helping solve the problem of financial inclusion in the U.S. by developing data-driven tools with machine learning risk segmentation algorithms that successfully finds Prime risk borrowers from within the non-Prime customer base,” Wije said.

Aliya’s solutions are currently being utilized at a top 5 bank in the U.S. and for the last 3 years, has not only increased the number of customers served in the sub FICO 720 space, but has done so without increasing the risk footprint of the portfolio from Prime. Aliya’s data suggests that at least 35% of the FICO 720 to 600 segments are misclassified by the system and have Prime risk characteristics.

Bottom line is that data is richer and more accurate than what is currently available to lenders and machine learning makes it even more powerful. The new paradigm is data-driven intelligence, augmented by human intelligence to drive dynamic lending solutions to borrowers is the future.

Wije closed the session with this gem: “Hiding behind regulation is not an excuse because they want to find ways to improve risk assessment and management too. In fact, one senior regulator told us that they have been waiting for the advanced analytics world to come up with something like this”.

For more information, Aliya welcomes conversations at: comments@aliya.com
Be sure to check out their website for more information.

Next up in our recap series from the Strum Executive Innovation Summit, we’ll hear from Chris Hopen, CEO at Strivve.

— John Mathes, Director of Brand Strategy, Strum