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Retail credit scoring using fine-grained payment data

Ellen Tobback and David Martens

Working Papers from University of Antwerp, Faculty of Business and Economics

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.

New Economics Papers: this item is included in nep-ban, nep-big, nep-pay and nep-rmg
Date: 2017-10
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