IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2104.05831.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2104.05831
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marcel Fafchamps & Julien Labonne, 2017. "Do Politicians’ Relatives Get Better Jobs? Evidence from Municipal Elections," The Journal of Law, Economics, and Organization, Oxford University Press, vol. 33(2), pages 268-300.
    2. Nathan, Max & Rosso, Anna, 2014. "Mapping information economy businesses with big data: findings from the UK," LSE Research Online Documents on Economics 60615, London School of Economics and Political Science, LSE Library.
    3. de Pedraza, Pablo & Vollbracht, Ian, 2020. "The Semicircular Flow of the Data Economy and the Data Sharing Laffer curve," GLO Discussion Paper Series 515, Global Labor Organization (GLO).
    4. Matteo Iacopini & Carlo R.M.A. Santagiustina, 2021. "Filtering the intensity of public concern from social media count data with jumps," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1283-1302, October.
    5. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    6. Resce, Giuliano & Vaquero-Piñeiro, Cristina, 2022. "Predicting agri-food quality across space: A Machine Learning model for the acknowledgment of Geographical Indications," Food Policy, Elsevier, vol. 112(C).
    7. Katsuyuki Tanaka & Takuji Kinkyo & Shigeyuki Hamori, 2018. "Financial Hazard Map: Financial Vulnerability Predicted by a Random Forests Classification Model," Sustainability, MDPI, vol. 10(5), pages 1-18, May.
    8. Doumpos, Michalis & Zopounidis, Constantin & Gounopoulos, Dimitrios & Platanakis, Emmanouil & Zhang, Wenke, 2023. "Operational research and artificial intelligence methods in banking," European Journal of Operational Research, Elsevier, vol. 306(1), pages 1-16.
    9. Abu Taher, Sheikh & Uddin, Md. Kama, 2018. "Use of big data in financial sector of Bangladesh – A review," 22nd ITS Biennial Conference, Seoul 2018. Beyond the boundaries: Challenges for business, policy and society 190348, International Telecommunications Society (ITS).
    10. Marieke Bos & Emily Breza & Andres Liberman, 2018. "The Labor Market Effects of Credit Market Information," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2005-2037.
    11. Mewse, Avril J. & Lea, Stephen E.G. & Wrapson, Wendy, 2010. "First steps out of debt: Attitudes and social identity as predictors of contact by debtors with creditors," Journal of Economic Psychology, Elsevier, vol. 31(6), pages 1021-1034, December.
    12. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    13. Nathan, Max & Rosso, Anna & Bouet, Francois, 2014. "Mapping 'Information Economy' Businesses with Big Data: Findings for the UK," IZA Discussion Papers 8662, Institute of Labor Economics (IZA).
    14. Whitaker, Stephan D., 2018. "Big Data versus a survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 285-296.
    15. Jin-Hyuk Kim & Tin Cheuk Leung, 2013. "Quantifying the Impacts of Digital Rights Management and E-Book Pricing on the E-Book Reader Market," Working Papers 13-03, NET Institute.
    16. Nofsinger, John R., 2012. "Household behavior and boom/bust cycles," Journal of Financial Stability, Elsevier, vol. 8(3), pages 161-173.
    17. Tax Research Team, 2016. "Demonetisation: Impact on the Economy," Working Papers 16/182, National Institute of Public Finance and Policy.
    18. Aur'elien Ouattara & Matthieu Bult'e & Wan-Ju Lin & Philipp Scholl & Benedikt Veit & Christos Ziakas & Florian Felice & Julien Virlogeux & George Dikos, 2021. "Scalable Econometrics on Big Data -- The Logistic Regression on Spark," Papers 2106.10341, arXiv.org.
    19. Delogu, Marco & Lagravinese, Raffaele & Paolini, Dimitri & Resce, Giuliano, 2024. "Predicting dropout from higher education: Evidence from Italy," Economic Modelling, Elsevier, vol. 130(C).
    20. Schicks, Jessica, 2014. "Over-Indebtedness in Microfinance – An Empirical Analysis of Related Factors on the Borrower Level," World Development, Elsevier, vol. 54(C), pages 301-324.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2104.05831. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.