Research on Risk Identification and Early Warning Models for Fintech Crimes Based on Big Data Analysis

Abstract

In response to the diverse evolution of fintech crimes—manifesting in behavioral pathways, identity spoofing, and cross-platform attacks—this study investigates a multi-stage risk identification and early warning model integrating big data analytics. The model achieves hierarchical identification of anomalous accounts through structural feature screening, deep modeling of temporal behaviors, and a Stacking ensemble strategy. It further incorporates a dynamic threshold mechanism and an online self-learning module to enhance adaptability. A testing platform under real business conditions was constructed. Comparisons with models such as GBDT, BiLSTM, and Transformer showed that the proposed model achieved an AUC of 0.941 and an F1-score of 0.893, with a significantly lower standard deviation than other methods, demonstrating strong stability and robustness.