Credit Tech
Alexander Agrabat Miller had long been interested in using modern statistical methods to process information found on the web, and he wrote a thesis and research projects on the value that could be unlocked by harvesting web data and using it to make predictions. He first looked at hedge funds and financial markets, but then he discovered credit scoring and the lack of credit data. He saw it as another application where those tools and data could add a lot of value.
Credit Tech built its own credit rating technology that relies heavily on alternative data and machine learning, which matters because many consumers lack credit bureau data. Alexander explained that the aim was not to refine bank underwriting but to make credit available to people who would otherwise fail bank approval because of income patterns or a missing credit history.
The company's core segment is consumers, and its average product is an unsecured loan. Alexander said many customers use the funds for large purchases, medical expenses, or unexpected shortfalls, and that for many of them Credit Tech is the only available source of finance. The company also serves some small and micro business owners, though consumers are its key focus.
Credit Tech prices loans on a risk-based basis. Its entry product is a loan of up to one month at around 30% cost of credit, while its personal installment loans go as low as 12% per year. Alexander said the company expects a low double-digit percentage of borrowers not to repay, and it offers a path to better terms: customers who repay on time for their first, second, and third transactions receive significantly better loan offers. Customers who share additional data can also get revised loans.
By November 2016, Credit Tech had originated more than one million loans since 2012 and had about $60 million in outstanding receivables. Most loans are short, averaging about 10 months, so the book turns over quickly. The company broke $100 million in originations for 2015 and reported revenue of a bit more than $40 million for that year, earned mainly through interest. It has raised over $120 million in equity, with investors including IFC, the World Bank, and Peter Thiel, and it funds lending partly through a securitization program that packages and sells loans to institutional investors.
- •Proprietary alternative-data credit scoring let the company approve borrowers that banks reject, creating a market with little competition.
- •Risk-based pricing lets the company lend to high-risk customers at high rates while still offering low-rate loans to lower-risk ones.
- •A path to better terms for customers who repay on time turns a high-cost first loan into a relationship that can grow and retain borrowers.
- •Short average loan duration of about 10 months lets capital recycle quickly, which supports fast growth on a limited balance sheet.
- •Funding through equity and a securitization program let the company grow its balance sheet without becoming a bank subject to capital requirements.
- 1.Identify a population that a standard gatekeeper, such as bank underwriting, systematically excludes, and confirm there is a real need for credit among them.
- 2.Build a proprietary risk model using non-traditional data sources where the incumbent data is missing, and validate it against actual repayment outcomes.
- 3.Price each customer individually based on assessed risk, starting with a higher-cost entry product for customers with thin data.
- 4.Create a clear graduation path where timely repayment unlocks better terms, and offer a way for customers to share more data to improve their rate.
- 5.Keep loan durations short so capital turns over quickly, and plan financing through a mix of equity and asset sales or securitization.
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