In 2010, roughly 8.3% of US consumers, or 19 million people, were considered ‘unscorable’ by FICO, a credit rating service whose models serve as a key underpinning of the US system for assessing credit worthiness. Included in this contingent of unscorable consumers are those lacking a credit history, read: millennials, who represent an untapped market opportunity for lenders. Also excluded from FICO are the underbanked, those who lack bank accounts and primarily transact in cash. Not only are the underbanked denied access to loans based on typical FICO filters, but they must resort to predatory alternatives, such as payday loans, whose prohibitively high interest rates trap them in a vicious cycle of deep indebtedness that is difficult to dig out of. Has the FICO score become an obsolete filter? And can a lender step in to provide loans to these underserved segments where traditional banks have neglected to do so – and profitably?
ZestFinance believes so, and has leveraged machine learning to develop a credit scoring engine for borrowers who lack the available credit history necessary for a suitable FICO score, if they have one at all. By assessing whether an individual should receive a loan and directing the applicant to a bank partner, ZestFinance addresses a market failure where underserved segments couldn’t access needed loans at interest rates they could afford – and that banks could afford to service.
ZestFinance offers two services to address this need. First, it licenses its credit-scoring services to subprime lenders with loans averaging around $600 with an APR of 390% — compare this to an average 521% for payday loans.  Second, the company has begun originating its own loans, called Basix, in the near prime space for consumers with FICO scores below 680. These loans aren’t cheap either, with a fixed annual interest rate of 26 to 36% over 3 years for loan sizes ranging from $3,000 to $5,000. 
The company achieves this value creation through core assets of 1) an experienced data science team with niche expertise in feature engineering, specifically for credit scoring and 2) a rich dataset of alternative credit assessment variables and loan performance outcomes. The latest algorithmic techniques are applied to a dataset of some 70,000 variables derived in part from 10 alternative credit bureaus that payday lenders report to. Interestingly, a loan applicant’s use of all capital letters on a loan application has been found to correlate with higher risk. ZestFinance’s accumulated dataset of the loans it has underwritten and associated loan performance can then be used to continuously hone and retrain its models.
Just how good are its models? Their subprime loans have achieved a 15% default rate, 50% the average figure for typical payday lenders. The company’s success has attracted the attention of Baidu and JD.com, which have enlisted its talents to develop a credit score for Chinese consumers based on alternate variables such as search history.
ZestFinance captures value through license deals with lenders and loan margin on its near prime loans, but one could see how these revenue streams could erode as the space becomes increasingly crowded and as policy further develops to protect subprime customers from interest rates beyond a certain threshold. Additionally, we have yet to really stress test these loans. It will be interesting to see how loan performance shakes out in the event of a downturn, especially in the case of their Basix loans, which have yet to realize a full 3-year term.
So what’s next?
ZestFinance competes in a crowded space, and key to its long-term defensibility will be its development of a truly proprietary dataset and ancillary services to support its data feedback loop. Joined by Prosper, Elevate Credit, Avant, and LendUp, just to name a few, ZestFinance’s claim to sophisticated human judgment in model building and valuable data are core competencies cited by all competitors. Its access to China’s wider diversity of model inputs positions it to develop a more unique dataset in the short term, but this could erode in the future.
Likely aware of this, ZestFinance has expanded beyond loan underwriting, and is now offering a new service called ZAML, which is a platform lenders can use to build their own credit-scoring models. Clients can input their own data sources, train models, and understand model outputs in economic terms through ZAML.
ZestFinance’s value creation story now has changed to involve value sharing across various players in the ecosystem. Best practices with data sources and modeling techniques can be potentially leveraged across ZAML’s entire client base to improve credit scoring practices – and with significantly less investment from each individual firm. ZAML could also be used to ensure model compliance with federal regulations, such as the CFPB’s mandate that lenders may not discriminate against certain demographics in their loan approval procedures.  Value capture now takes the form of recurring SaaS revenue for ZestFinance. Key questions going forward will be whether the company can sustain a recurring need for its ZAML platform – are models built once and rarely retrained—and whether there is an opportunity to horizontally extend its services across industries.