A new anomaly detection via multiple instance learning for sequence data with application to credit card delinquency risk control

Anomaly detection in sequence data is widely applicable across various domains and has significant commercial value to the financial industry. This paper studies its utility as a means of controlling credit card delinquency risk. Transactions that deviate from the regular data sequence are a common precursor of payment difficulty. Current detection methods, however, do not effectively identify abnormal transactions from such data, making it difficult to control the overdue payment risk. Therefore, in this paper, we propose a Multiple Instance Learning-based Anomaly Detection (MILAD) method with well designed learning networks to address this problem. Comparing the performance of the MILAD and Deep Autoencoding Gaussian Mixture Model (DAGMM) method, which is currently the most commonly used unsupervised deep learning algorithm for credit card risk control, we observe that the proposed MILAD is able to effectively control the overdue risk by leveraging both transaction and payment information.