Dialogue to Detection: A Multimodal Hybrid NLP Pipeline for Insurance Fraud Detection

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.