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

Author

Listed:
  • Gabriel Suarez
  • Juan Raful
  • Maria A. Luque
  • Carlos F. Valencia
  • Alejandro Correa-Bahnsen

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.

Suggested Citation

  • Gabriel Suarez & Juan Raful & Maria A. Luque & Carlos F. Valencia & Alejandro Correa-Bahnsen, 2021. "Enhancing User' s Income Estimation with Super-App Alternative Data," Papers 2104.05831, arXiv.org, revised Aug 2021.
    • Handle: RePEc:arx:papers:2104.05831
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    References listed on IDEAS

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    1. Tokunaga, Howard, 1993. "The use and abuse of consumer credit: Application of psychological theory and research," Journal of Economic Psychology, Elsevier, vol. 14(2), pages 285-316, June.
    2. Liran Einav & Jonathan Levin, 2014. "The Data Revolution and Economic Analysis," Innovation Policy and the Economy, University of Chicago Press, vol. 14(1), pages 1-24.
    3. Luisa Roa & Alejandro Correa-Bahnsen & Gabriel Suarez & Fernando Cort'es-Tejada & Mar'ia A. Luque & Cristi'an Bravo, 2020. "Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications," Papers 2005.14658, arXiv.org, revised Jan 2021.
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