Enhancing User' s Income Estimation with Super-App Alternative Data
This work addresses the need for more accurate income estimation for financial institutions, though it is incremental as it builds on existing methods by incorporating new data sources.
The paper tackled the problem of user income estimation by comparing alternative data from Super-Apps with traditional bureau income estimators, showing that alternative data captures additional information and improves predictive power, as highlighted using the TreeSHAP method for interpretation.
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.