Abstract: Microcredit refers to small loans to borrowers who typically lack collateral, steady employment, or a verifiable credit history. It is designed not only for start-ups but also for individuals. The microcredit industry is experiencing fast growth in China. In contrast with traditional loans, microcredit typically lacks collateral, which makes credit scoring important. Due to the fast development of on-line microcredit platforms, there are various sources of data that could be used for credit evaluation. Among them, bank card transaction records play an important role. How to conduct credit scoring based on this type of data becomes a problem of importance. The key issue to be solved is feature construction: how to construct meaningful and useful features based on bank card transaction data. To this end, we propose here a so-called RFMS method. Here "R" stands for recency, "F" stands for frequency, and "M" stands for monetary value. Our method can be viewed as a natural extension of the classical RFM model in marketing research. However, we make a further extension by taking "S" (Standard Deviation) into consideration. The performance of the method is empirically tested on a data example from a Chinese microcredit company.

Key words and phrases: Credit scoring, frequency, logistic regression, microcredit, monetary value, recency, standard deviation.