Generative AI for Claims Evidence Interpretation and Fraud Analysis

Abstract

The insurance claims process generates vast volumes of unstructured evidence that present substantial challenges for human adjudicators to analyze comprehensively and consistently. Generative artificial intelligence has emerged as a transformative technology for automating evidence interpretation and fraud detection across the insurance industry. Large language models process narrative evidence from claims descriptions, witness statements, and medical records to extract key facts and identify inconsistencies. Vision transformer architectures analyze claims imagery, including property damage photographs and accident scene documentation, to detect manipulation and assess damage severity. Multimodal transformer architectures integrate textual and visual information simultaneously, enabling correlation between written descriptions and photographic evidence. Fraud detection employs supervised machine learning models trained on historical claims data, unsupervised anomaly detection systems, and behavioral pattern analysis. Generative AI systems reduce document review time substantially while improving fraud detection accuracy when augmenting traditional rule-based indicators. Synthetic data generation addresses data scarcity challenges by creating realistic fraudulent claim examples for training purposes. However, significant technical challenges persist, including hallucinations where models generate factually incorrect information, reduced generalizability in fine-tuned models, adversarial attacks, and bias risks. Explainability requirements demand transparent reasoning for fraud flagging decisions through attention mechanism visualization and feature importance measures. Insurance regulators are putting more pressure on transparency and auditability in automated claims decisions, which will require full documentation of the decision and testing for bias across demographic categories. Privacy issues also require the safeguarding of sensitive policyholder data. Success will require balancing the recent and transformative capabilities of ethics and governance, oversight by humans, and regulatory compliance, which will be necessary to ensure fairness and accuracy in claims processing.