Smart Agents for Risk and Fraud Intelligence in Insurance
Keywords:
Agentic AI, Insurance Fraud Detection, Risk Prediction, Predictive Analytics, Fraud Probability Scoring, Abnormality Detection, Insurance Data Platforms, AI-Driven Risk Intelligence, Collaborative Decision-Making, Fraud Risk Ranking, Customer Experience Optimization, Intelligent Insurance Analytics, AI Governance in Insurance, Multi-Agent AI Systems, Fraud Detection Models.Abstract
A concise objective, evidence-based abstract for a study of agentic AI-powered risk prediction and fraud detection for insurance data platforms. By facilitating shared decision-making by underwriters and data scientists, agents, and risk, fraud, and customer experience teams, it is expected that agentic AI will improve predictive performances, generate richer abnormality detection signals, and outperform traditional AI as a risk and fraud detection tool. Empirical results from an insurance portfolio sample support this hypothesis, demonstrating agentic AI predictive accuracy, good fraudulent event discrimination, and sensitivity and specificity scores exceeding existing solutions. Although detecting fraud remains challenging, agentic AI ranks customers by the likely probability of fraud occurring in the next month, supporting team priorities.
The functioning and convergence of different teams, systems, and data within insurers are essential foundations to improve alerts received and minimize impact on customer experience. Agentic AI diverges strategically and functionally from traditional AI, which is based on automating decisions and minimizing human intervention. Agency incorporates the latest AI methodologies, progressing a step further by aligning data science and business teams hierarchically. Agentic AI performs as well or better than non-agentic when responding to the same alert signals, enabling detection of potential future frauds while refining customer experience. Future research prompts should explore governance aspects and the value of agents for companies.
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