Financial Fraud Detection on Micro-credit Loan Scenario via Fuller Location Information Embedding

Junxiang Jiang, Boyi Ni, Chunping Wang

Abstract: Micro-credit loan serves as an indispensable supplementary loan for people lacking verifiable credit records or unqualified to get conventional bank loans. And financial fraud detection is one of the mainstream methods to control loan risk. Its goal is to utilize a set of corresponding features (e.g., customer behaviors) to predict whether a customer will fail to make required payments in the future. To the best of our knowledge, few works pay attention to permanent residential locations of customers. However, the real data study shows that customer location information potentially provides additional power in financial fraud detection. Three challenges place barriers to make full use of location information: (1) Data sparsity makes financial fraud detection model hard to learn the relationship between location information and fraud behaviors; (2) Financial fraud detection model considering location information alone without resident personality, might weaken fraud distinguish power of location information; (3) The representation of location information should be effective and easy to apply, for being used in various applications. In this paper, we propose Fuller Location Information Embedding (FLIE) network. FLIE ideally handles above challenges, which performance verified by experiments on the tasks of fraudulent customer prediction and customer segmentation.