Artificial Intelligence Application in Financial Customer Fraud Detection

With the rapid digitization of financial services, the online transactions have become prevalent, accompanied by a growing number of financial fraud incidents that cause substantial losses to financial institutions and individual users. Traditional rule-based fraud detection methods suffer from low efficiency, poor timeliness and limited adaptability to hidden and emerging fraud behaviors. Benefiting from the powerful self-learning capability, high-dimensional data processing performance and dynamic feature mining advantages of artificial intelligence algorithms, intelligent detection technologies have become the mainstream solution for modern financial fraud risk control. This paper systematically investigates the application of typical machine learning and deep learning algorithms in customer financial fraud detection. It summarizes the practical advantages of different models, analyzes the core application challenges including the data imbalance and poor model interpretability, and proposes targeted future development directions. The research aims to provide the theoretical references for the optimization and industrial application of intelligent financial fraud detection systems.