  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.
