Working Paper: NBER ID: w24551
Authors: Tobias Berg; Valentin Burg; Ana Gombovi; Manju Puri
Abstract: We analyze the information content of the digital footprint – information that people leave online simply by accessing or registering on a website – for predicting consumer default. Using more than 250,000 observations, we show that even simple, easily accessible variables from the digital footprint equal or exceed the information content of credit bureau scores. Furthermore, the discriminatory power for unscorable customers is very similar to that of scorable customers. Our results have potentially wide implications for financial intermediaries’ business models, for access to credit for the unbanked, and for the behavior of consumers, firms, and regulators in the digital sphere.
Keywords: Fintech; Credit Scoring; Digital Footprints; Consumer Defaults
JEL Codes: D12; G20; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| digital footprint variables (C91) | consumer default (G33) |
| digital footprint variables (C91) | income (E25) |
| digital footprint variables (C91) | character (Y60) |
| digital footprint variables (C91) | reputation (M14) |
| digital footprint variables + credit bureau information (G51) | consumer default (G33) |
| digital footprint variables (C91) | discriminatory power for unscorable customers (C52) |
| digital footprint variables + credit bureau information (G51) | predictive accuracy (C52) |
| digital footprint variables (C91) | default rates (E43) |