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Retail credit scoring using fine‐grained payment data

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  • Ellen Tobback
  • David Martens

Abstract

Banks are continuously looking for novel ways to leverage their existing data assets. A major source of data that has not yet been used to the full extent is massive fine‐grained payment data on the bank's customers. In the paper, a design is proposed that builds predictive credit scoring models by using the fine‐grained payment data. Using a real life data set of 183 million transactions made by 2.6 million customers, we show that the scalable implementation that is put forward leads to a significant improvement in the receiver operating characteristic area under the curve, with only seconds of computation needed. When investigating the 1% riskiest customers, twice as many defaulters are detected when using the payment data. Such an improvement has a big effect on the overall working of the bank, from applicant scoring to minimum capital requirements.

Suggested Citation

  • Ellen Tobback & David Martens, 2019. "Retail credit scoring using fine‐grained payment data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1227-1246, October.
    • Handle: RePEc:bla:jorssa:v:182:y:2019:i:4:p:1227-1246
    DOI: 10.1111/rssa.12469
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    Cited by:

    1. Kyriakos Georgiou & Athanasios N. Yannacopoulos, 2023. "Probability of Default modelling with L\'evy-driven Ornstein-Uhlenbeck processes and applications in credit risk under the IFRS 9," Papers 2309.12384, arXiv.org.
    2. Königstorfer, Florian & Thalmann, Stefan, 2020. "Applications of Artificial Intelligence in commercial banks – A research agenda for behavioral finance," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    3. Elena Deryugina & Alexey Ponomarenko & Andrey Sinyakov, 2021. "Exploring the conjunction between the structures of deposit and credit markets in the digital economy under information asymmetry," Bank of Russia Working Paper Series wps78, Bank of Russia.
    4. Michael Bucker & Gero Szepannek & Alicja Gosiewska & Przemyslaw Biecek, 2020. "Transparency, Auditability and eXplainability of Machine Learning Models in Credit Scoring," Papers 2009.13384, arXiv.org.

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