Strengthening Fraud Prevention with AI in P&C Insurance: Enhancing Cyber Resilience
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V2I1P106Keywords:
AI in insurance, fraud detection, P&C insurance, cyber resilience, machine learning, deep learning, anomaly detection, natural language processing, graph analytics, cybersecurity, risk managementAbstract
The Property and Casualty (P&C) insurance sector is also experiencing an ever more complicated fraud environment driven by digitalization, and the increased number of cyber risks. Old rule-based systems have become inadequate to identify complex fraud or allow high-level cybersecurity. The paper discusses how the P&C insurance industry can monitor fraud prevention and improve cyber resilience through Artificial Intelligence (AI) technologies. Insurers will be able to detect anomalies proactively, identify fraud rings, and analyze volumes of both structured and unstructured high-value, real-time data, enabled by inference on machine learning, deep learning, natural language processing, and graph analytics. The accuracy of fraud detection, efficiency in the processing of claims and early severity of threats can be measured using case studies and show positive gains. Artificial intelligence also contributes significantly to the enhancement of cybersecurity, helping insurers to keep track of network activities, dynamic evaluation of risks, and rapid response in case of an actual breach. Data quality and interpretability of models, privacy aspects, and scalability remain key issues, although they may be addressed as the AI technology advances. In this paper, the limitations are discussed with indications of projecting directions, namely, federated learning, autonomous cyber response systems, and developing regulatory frameworks. Finally, the application of AI in fraud and cyber risk management not only minimizes operational losses but also enhances operational trust, compliance and resilience in an ever-increasing digital insurance environment
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