Hybrid AI for Insurance Fraud Detection
DOI:
https://doi.org/10.5281/zenodo.22172709Keywords:
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 poses ongoing operational and financial challenges to the insurance industry, which rely on expert knowledge to prepare for and react against ever-evolving attacks. Beyond discrimination of potential fraud scenarios, future detection systems must therefore support the synthesis of training data for evolving patterns, surface latent signals for unseen forms, propose tests for the detection of adaptative fraud patterns and scenarios for benchmarking, and automatically adapt to changes. Recent advancements in the fields of Generative AI and Autonomous Decision making propose highly complex agents able to reason around natural language, map textual to 3D spaces, and learn new object categories from a few examples. Such models can find application to these aspects, and a study plan explore the hybrid integration of Generative and Agentic AI.
The research takes the shape of a Do-Observe-Learn cycle, with the first cycle concentrating on Detect, along with Detect processes for Synthesis, Adaptation, and Quality Layer, thus applying the original focus of fraud detection to the new context, identifying the critical elements from which new decisions can be made, concluding with the resulting TDCC basis for realisation. An analysis on how evolving patterns can influence the design is also performed, targeting integration path of the hybrid design, with the generative component directly involved in detection pipeline in layer supporting adaptation. One important instance of integration focuses on the Data Quality Layer, combining external knowledge with the concept of absolutely difficult-to-detect forms for fraud detection, thus synthesizing signals that should be present in the data for the adapted detector to generalise the aspect.
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Copyright (c) 2023 Dimitris Plexousakis (Author)
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