Adaptive Fraud Detection via Hybrid AI in Insurance
DOI:
https://doi.org/10.5281/zenodo.22830458Keywords:
Insurance Fraud Detection, Generative AI for Fraud, Agentic AI Systems, Adaptive Fraud Detection, Fraud Pattern Synthesis, Autonomous Decision Systems, Hybrid AI Architectures, Fraud Signal Discovery, Synthetic Training Data, Data Quality in Fraud Detection, Fraud Scenario Simulation, Evolving Fraud Patterns, Fraud Detection Pipelines, Anomaly Detection in Insurance, AI-Driven Risk Detection, Do-Observe-Learn Cycle, Fraud Benchmarking Models, Intelligent Fraud Analytics, Self-Adaptive Detection Systems, Advanced Fraud Prevention.Abstract
Fraud remains a persistent operational and financial challenge for the insurance industry, which continues to rely heavily on expert knowledge to anticipate and respond to constantly evolving attack patterns. Beyond simply classifying potential fraud scenarios, next-generation detection systems must also be able to synthesize training data for emerging fraud patterns, surface latent signals for previously unseen fraud types, generate test cases for detecting adaptive fraud behavior, create benchmarking scenarios, and adapt automatically as patterns change over time.
Recent advances in Generative AI and Autonomous Decision-Making have produced highly sophisticated agents capable of reasoning over natural language, mapping textual descriptions to spatial representations, and learning new object categories from only a few examples. These capabilities are directly applicable to the challenges outlined above, motivating this study's exploration of a hybrid integration of Generative and Agentic AI for fraud detection.
This research is structured as a Do–Observe–Learn cycle. The first iteration focuses on the Detect phase, encompassing supporting processes for Synthesis, Adaptation, and a Data Quality Layer — extending traditional fraud detection into this new hybrid framework. This phase identifies the critical elements needed to inform downstream decisions, culminating in a Test-Driven Continuous Calibration (TDCC) foundation for implementation.
The study further examines how evolving fraud patterns influence system design, with particular attention to integration pathways within the hybrid architecture — specifically, how the generative component can be embedded directly within the detection pipeline to support adaptation. One key integration point is the Data Quality Layer, which combines external domain knowledge with the concept of inherently hard-to-detect fraud cases. This synthesizes the signals that should be present in the data, enabling the adapted detector to generalize effectively to these difficult cases.
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