Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications

TL;DR

This paper explores how app-based marketplace data enhances credit risk models, especially for underserved groups, revealing non-linear patterns and regulatory considerations for leveraging alternative data sources.

Contribution

It demonstrates the predictive power of app-based data in credit scoring, especially for low-wealth and young individuals, and highlights interpretability and regulatory challenges.

Findings

Alternative data improves credit prediction for underserved groups.

TreeSHAP reveals non-linear variable trends from app data.

Data sources can disrupt traditional banking models.

Abstract

In this paper we present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models. These alternative data sources have shown themselves to be immensely powerful in predicting borrower behavior in segments traditionally underserved by banks and financial institutions. Our results, validated across two countries, show that these new sources of data are particularly useful for predicting financial behavior in low-wealth and young individuals, who are also the most likely to engage with alternative lenders. Furthermore, using the TreeSHAP method for Stochastic Gradient Boosting interpretation, our results also revealed interesting non-linear trends in the variables originating from the app, which would not normally be available to traditional banks. Our results represent an opportunity for technology companies to disrupt traditional banking by correctly identifying alternative data sources and handling this new information properly. At the same time alternative data must be carefully validated to overcome regulatory hurdles across diverse jurisdictions.