Defense strategies against data poisoning attacks in AI financial risk control models
Yina Liu · Advances in Engineering Innovation · 2025
Against the backdrop of rapid fintech development, Artificial Intelligence(abbreviated as AI)financial risk control models have been widely applied in financial risk assessment and management due to their efficiency and accuracy. However, data poisoning attacks, as a malicious means targeting model training data, severely threaten the reliability and security of these models. From a professional and technical perspective, this paper deeply analyzes the principles of AI financial risk control models and data poisoning attacks, systematically sorts out the existing problems in the current response process, including incomplete data source verification mechanisms, insufficient abnormal data identification capabilities, to be improved model robustness, imperfect dynamic defense systems, and lagging attack traceability technologies. Aiming at these issues, specific response strategies are proposed, such as constructing a multi-dimensional data verification system, strengthening abnormal data detection algorithms, optimizing model architecture design, establishing dynamic monitoring and response mechanisms, and enhancing attack traceability technology, with the aim of providing theoretical and practical references for ensuring the safe and stable operation of AI financial risk control models.