Improving Policy Integrity with AI: Detecting Fraud in Policy Issuance and Claims
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I1P111Keywords:
Fraud detection, Claims fraud, Machine learning, Deep learning, Anomaly detection, Natural language processing, explainable AIAbstract
The rapid development in the complexity and size of insurance fraud, especially in policy issuance and in processing claims, challenges the global insurance industry significantly. Existing rule-based and manual methods of detection cannot be used to identify new patterns of fraud and thus lead to losses, inefficiency in operations, and low trust in policyholders. This paper discusses the prospects of efficient employment of artificial intelligence (AI) in improving the detection of fraud and the enhancement of policy integrity within the insurance lifecycle. Our proposed AI-based model involves a combination of supervised and unsupervised machine learning, deep learning, anomaly detection, and natural language processing (NLP) activities that can be used in detecting and countering known and emerging fraud scenarios. A modular architecture of the system is developed, including real-time detection, feedback provision to update the model, as well as an ease of interjection with policy management systems. Experimental analyzes with a benchmark fraud dataset prove the supremacy of AIs on accuracy, precision, recall, and F1-score, with better performance by using the AI models, especially neural networks and ensemble methods. Besides that, we provide some ethical considerations, privacy compliance, and the importance of explainable AI (XAI) in establishing transparency and trust in automated decision-making models. Also considered are its future courses, such as the integration of blockchain technology for record-keeping that cannot be altered and smart contract automation. In this study, the transformative power of AI in terms of guaranteeing the security of insurance operations, enhancing the accuracy of detection, and futuristic fraud prevention tactics to make them proactive and scalable is highlighted
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