Retail credit scoring using fine‐grained payment data.

Summary: 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. [ABSTRACT FROM AUTHOR]