AI in Fraud Detection: Leveraging Machine Learning to Combat Insurance Fraud
DOI:
https://doi.org/10.56472/ICCSAIML25-109Keywords:
Insurance fraud, machine learning, fraud detection, artificial intelligence, supervised learning, unsupervised learning, anomaly detection, predictive analytics, claims processing, fraud preventionAbstract
An Increasing problem, insurance fraud causes yearly losses for businesses of billions of dollars & drives higher rates for actual policyholders. Usually based on rule-based methods that find it difficult to change with the dynamic strategies used by Scammers, traditional Scam detection systems For AI & ML, this is the Domain of intervention. By analyzing more amounts of data & exposing hidden tendencies, AI-driven Scam detection systems can find bogus claims with more speed & accuracy. ML models always improve by absorbing lessons from past events, therefore reducing false positives & identifying perhaps small-scale Misleading activity. Using methods like anomaly detection, natural language processing (NLP), & predictive analytics which help to identify misleading activity helps to improve productivity & reduce economic losses. The benefits of fraud detection motivated by AI go beyond simple financial savings. It minimizes unnecessary delays & speeds legitimate claims, therefore enhancing the user experience. As technology develops, insurance companies could expect ever more advanced solutions combining IoT, block chain, AI, and improved biometrics. Still, challenges remain including data privacy concerns & the need of openness in AI policy-making. Improving AI models & keeping their dominance against ever complex fraud schemes will depend on future research and invention. The insurance industry has to balance automation with human knowledge as AI use rises to maximize efficiency while maintaining trust and equity
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References
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