Dialogue to Detection: A Multimodal Hybrid NLP Pipeline for Insurance Fraud Detection
For insurance fraud detection researchers, this provides a reproducible multimodal baseline, though it is incremental as it combines existing techniques.
The paper introduces a synthetic multimodal framework for insurance fraud detection at FNOL, combining dialogue transcripts, audio, ASR, diarisation, NER, regex, LLM-RAG, and speaker embeddings into a rule-based risk score. It demonstrates stability and transfer potential, offering a reproducible baseline beyond text-only methods.
Insurance fraud imposes substantial financial losses and operational inefficiencies, raising premiums and impacting trust among legitimate policyholders. Early detection at FNOL remains a persistent challenge. Existing approaches rely largely on private, text-only datasets, limiting progress on multimodal methods that integrate linguistic, behavioural, and speaker-based indicators. We introduce a synthetic multimodal framework that replicates FNOL conditions. It generates agent-customer dialogue transcripts and two-speaker audios, performs ASR and diarisation. Downstream modules combine NER, regex-based feature extraction, LLM-RAG retrieval, and speaker embeddings in a rule-based risk score to flag narrative reuse, structural inconsistencies, and cross-case voice repetition while balancing sensitivity and false positives. Dataset validation and component-level evaluations show stability and transfer potential, offering a reproducible baseline beyond text-only fraud detection.