Fusing Generative AI and Agentic Workflows for Fraud Mitigation in InsurTech
Keywords:
Generative Artificial Intelligence,Agentic AI Systems,Fraud Detection,InsurTech,Machine Learning,Deep Learning,Explainable AI (XAI),Anomaly Detection,Predictive Analytics,Natural Language Processing (NLP),Multi-Agent Systems,Risk Assessment,Synthetic Data Generation,Real-Time Fraud Detection,Intelligent Decision Systems.Abstract
In a hybrid generative and agentic artificial intelligence architecture, generative AI generates fraud-detection signals based on historical events while agentic AI provides decision-suggestion commands to the human stakeholder in-the-loop. This synergistic approach overcomes the limitations of pure generative and agentic frameworks in detecting fraud patterns in modern InsurTech platforms. Rapidly growing digitalization and increased dependence on digital channels increase the susceptibility of insurance platforms to behavioral fraud. Advanced technologies such as AI and Machine Learning (ML) have the potential to mitigate the risk of billions of dollars lost through these frauds. To harness the power of both generative and agentic AIs, a novel hybrid system is proposed. Key stakeholders of the system include the InsurTech venture for which the system is being built, customers of the insurance platform, the development team, and the operational team responsible for maintaining and managing the AI pipelines.
Data aspects of the architecture encompass novel feature-engineering techniques, a sophisticated data-pipeline approach, and an incremental learning mechanism to prevent performance deterioration due to concept drift. An unsupervised-ML-based segmentation model has been instantiated for the generation of training datasets and enables scalable continuous-signal generation in a real-time production environment. The quality of signals in the fraud-detection domain is not always suitable for direct operationalization. Hence, the operational design leverages the hybrid nature of the architecture by enabling human evaluation of the suggestion signals in a real-time mode. The primary fraud-detection application of the system is claims related, although it has the capability to generate fraud signals for policy underwriting, channel fraud, and behavioral fraud use cases as well.
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