Enhancing User' s Income Estimation with Super-App Alternative Data

Gabriel Suarez, Juan Raful, Maria A. Luque, Carlos F. Valencia and Alejandro Correa-Bahnsen

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Abstract: This paper presents the advantages of alternative data from Super-Apps to enhance user' s income estimation models. It compares the performance of these alternative data sources with the performance of industry-accepted bureau income estimators that takes into account only financial system information; successfully showing that the alternative data manage to capture information that bureau income estimators do not. By implementing the TreeSHAP method for Stochastic Gradient Boosting Interpretation, this paper highlights which of the customer' s behavioral and transactional patterns within a Super-App have a stronger predictive power when estimating user' s income. Ultimately, this paper shows the incentive for financial institutions to seek to incorporate alternative data into constructing their risk profiles.

Date: 2021-04, Revised 2021-08
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