Robustness and Adversarial Resilience of Actuarial AI/ML Models in the Face of Evolving Threats

The application of artificial intelligence (AI) and machine learning (ML) in actuarial science yields data-driven financial decision-making processes, as well as transformed predictive modeling and risk assessment. Security threats that occur due to increasing AI/ML model adoption create significant risks for actuarial applications through data poisoning and both evasion techniques and model inversion attacks. Breach points in systems create substantial risks for misjudged risks, price distortions, and regulatory issues, which damage the dependability of actuarial modeling outcomes. Adversarial resilience and robustness of AI/ML models in actuarial science receive detailed exploration in this paper through assessments of existing defense mechanisms which primarily include adversarial training, anomaly detection and robust feature engineering methods as well as identification of main threat vectors. This paper covers the essential regulatory structures and ethical matters because such frameworks protect the integrity of trustable AI-driven actuarial systems. The effectiveness of various adversarial threat defenses against actuarial AI models is evaluated through experimental results. The research confirms that security measures in the actuarial domain of AI need ongoing development to protect its systems from current and future threats which require sustainable reliability and threat resistance.