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Retail credit scoring using fine-grained payment data

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  • TOBBACK, Ellen
  • MARTENS, David

Abstract

In this big data era, banks (like any other large company) are looking for novel ways to leverage their existing data assets. A major data source that has not been used to the full extent yet, is the massive fine-grained payment data on their customers. In this paper, a design is proposed that builds predictive credit scoring models 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 our proposed design adds complementary predictive power to the current credit scoring models. Such improvement has a big impact on the overall working of the bank, from applicant scoring to minimum capital requirements.

Suggested Citation

  • TOBBACK, Ellen & MARTENS, David, 2017. "Retail credit scoring using fine-grained payment data," Working Papers 2017011, University of Antwerp, Faculty of Business and Economics.
    • Handle: RePEc:ant:wpaper:2017011
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    References listed on IDEAS

    as
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