Alternative credit analytics use nontraditional data and techniques to assess a borrower’s credit. A commonly suggested, though controversial, example is the use of an applicant’s social media behavior to determine creditworthiness. However, meaningful insights can be derived from information as innocuous as the characters in an applicant’s email or the device in their pocket. This digital footprint—the information-trail people leave behind when accessing or registering on a website—can provide proxies for income, character and reputation that are highly valuable for default prediction.
Combining credit score data with simple digital footprint information delivers more powerful predictions of default risk than credit scores alone, according to the paper On the Rise of FinTechs – Credit Scoring Using Digital Footprints (Berg, Burg, Gombovic, & Puri, 2018). They found that a “digital footprint”—made up of just 10 variables any digitally active firm could collect—had the predictive power of traditional credit score data. While regulators are likely to closely monitor the use of alternative data, digital footprints demonstrate the promise of alternative credit analytics to enhance underwriting, lower costs, and expand access to credit.