Credit Anomaly Detection Method based on Bayesian Networks

Abstract

Due to the virtual nature of online transactions and the underdeveloped personal credit risk assessment system of Internet finance lending platforms in China, these platforms often struggle to verify the creditworthiness of applicants prone to fraud and default, leading to loan losses. As a result, risk control has become the core of Internet lending platforms. Modern risk control relies on statistical and data mining techniques to analyze and model data, uncover patterns that indicate loan defaults, and identify anomalies. This paper proposes a Bayesian network-based credit anomaly detection method, which detects anomalies by calculating and ranking the joint anomaly probabilities of sample instances. The technique operates under unsupervised learning and can effectively handle missing and imbalanced data. This method analyzes a loan credit dataset with 32,581 samples and 12 features of the customers. The result of the analysis demonstrates the feasibility and effectiveness of this method in detecting anomalies in online loans.