The Secret to Reviving Your Alternative Lending Business

It’s no secret times are changing for Small and Medium Enterprise (SME) lending. I think we can all agree the easy return on investment has given way to rising defaults and an origination engine that sounds like it’s barely chugging along. We all know the basic math in this business. Put lots of money on the street and hope the factor rates cover the losses while managing operating expenses to achieve a rate of return acceptable to private equity investors. So, when originations stall and defaults increase, the math becomes scary.

As the fintech business leader for DataRobot, the leader in automated machine learning, I’ve had the opportunity to meet many SME business owners at LendIt in New York, and more recently at the Innovate Finance Global Summit in London. It’s very clear interest in machine learning (a branch of artificial intelligence used by many lenders to build risk-based pricing models) is at an all-time high. We’re grateful for the support of our customers, and we take our leadership role in predictive analytics and automated machine learning very seriously. We don’t just see ourselves as a software vendor. We’re innovators that listen intently to our customers (and potential customers), and build solutions that help them achieve their goals. So, when I see this critical juncture in the SME lending industry, I feel compelled to comment.

I believe the response in the industry to the current problem of growing defaults and dwindling originations is the wrong approach. Lenders seem to be doing one of three things (and potentially all of them): lowering underwriting standards, raising factor rates, or pushing independent sales organizations (ISOs) to bring in more applications by handing out one-time commission bumps or in-kind resources such as marketing support. I believe these actions are indicative of lenders who don’t fully realize the wealth of data they have in their companies. Let’s review what I would call a “prototypical” merchant cash advance or business loan lender. First, as Sean Murray, publisher of deBanked, recently pointed out in a LinkedIn post (see, https://www.linkedin.com/feed/update/urn:li:activity:6253411111144685568/), most of the applications lenders receive are of very low quality, and in some cases are fraudulent. This will never be an industry where 50 percent of the applications will result in an offer. From the perspective of someone who talks to lenders almost every day, a company is lucky if 25 percent of its applicant pool can justify an offer. The fact is there just aren’t enough applicants in the industry to force that percentage to go much higher while still maintaining an acceptable default rate. And in terms of default rate, I’d question any lender who says their default rate is single digits. What that leaves us with is the number of applications which are approved and result in a closed deal (i.e., the “win-rate”). And this is where lenders are missing opportunities.

If lenders want to win in this industry, they need to win the right kind of deals that work for their individual business. If all lenders pursue the same strategy of pushing originations and taking on more risks, then many lenders are going to fail because a one-size fits all response to the current funding environment doesn’t work because there is no such thing as a one-size fits all portfolio. For example, if you specialize in “C” and “D” paper in the 3rd or 4th lien position, you want to win deals that look much different than lenders who want “A” paper in the 1st or 2nd lien position. Knowing how to increase your “win-rate” for YOUR type of deals is the key to success. And the key to the lender winning more of their type of deals is being able to understand their underwriting history and immediately spot deals that fit their framework, price them accordingly, and aggressively secure them before the competition. And that only happens if lenders understand their own data and have a risk-based pricing system which is lightning fast and makes the right decisions.

I’m not going to tell every lender to pursue automated machine learning. If a lender is brand-new to the market and isn’t keeping their data for analytical purposes, then deploying advanced machine learning algorithms is an inferior solution to good old-fashioned rules-based underwriting. But for everyone else, here is something you need to know. Many private equity funds are using machine learning algorithms to allocate their investments. It doesn’t take much imagination to consider those funds may start asking themselves why they are using machine learning to allocate their investments to companies not using machine learning to manage their risk exposure. The key to achieving success in a tightening market such as SME lending is to intimately understand your business. There is no more room for one-size fits all strategies. Consider what would happen if you simply doubled your “win-rate” for the deals that fit your business without looking at more applications? You would start spending a lot less time beating the originations drum and have more time to deliver superior customer service to increase your renewal rate. Automated machine learning can make that happen.

Justin B. Dickerson, PhD, MBA, PStat is the General Manager of Fintech for DataRobot. He’s also a former Chief Data Scientist of a merchant cash advance funder and a machine learning speaker, author, and enthusiast. Email him at justin.dickerson@datarobot.com