Super-app behavioral patterns in credit risk models: Financial, statistical and regulatory implications. (1st May 2021)

• Record Type: Journal Article
• Authors: Roa, Luisa; Correa-Bahnsen, Alejandro; Suarez, Gabriel; Cortés-Tejada, Fernando; Luque, Maria A.; Bravo, Cristián
• Is Part Of: Expert systems with applications. Volume 169(2021)
• Publication Date: 2021-05-01
• Subjects: Fintech -- Super-app -- Credit sscoring -- Financial inclusion -- Alternative data; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.114486
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: Super-apps alternative data captures information that a traditional bureau does not. Super-apps data contributes to financial inclusion and credit assessment. Statistical and financial performance of credit models improves with alternative data. Regulators should encourage the use of alternative data with an effective supervision. 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.