2020 Volume 49 Issue 2 Pages 217-240
Banks employ attribute information such as income, family status, work status to screen borrowing applicants for personal card loans in general. Personal behavior characteristics may also affect defaults but are not taken into account. In this paper, we analyze them using deposit-withdrawal data of bank account, and construct a model of evaluating default for the purpose of screening the applicants for card loans. We prepare some variables related to personal behavior characteristics such as number of commission payments, average deposit balance, peak balance ratio, using about 7.6 million data, and analyze the relationship with default. In addition, we construct a logit model with these variables, and examine the model using accuracy ratio (AR). The result shows that the AR exceeds 50%, and we find the model is effective in practice. We also confirm the robustness of the results through out-of-sample test and cross-validation.
