Problem
Research questions and friction points this paper is trying to address.
insurance fraud detection
multimodal
FNOL
early detection
speaker-based indicators
Dialogue to Detection: A Multimodal Hybrid NLP Pipeline for Insurance Fraud Detection
🤖 AI Summary
Insurance fraud is notoriously difficult to detect early during the First Notice of Loss (FNOL) stage, and existing approaches are largely confined to private, text-only data, lacking the capacity to integrate linguistic, behavioral, and voice-based cues. This work proposes the first reproducible synthetic multimodal benchmark for fraud detection, simulating FNOL scenarios to generate agent–customer dialogue transcripts and dyadic speech recordings. By combining automatic speech recognition, speaker diarization, named entity recognition, rule-based feature extraction, LLM-augmented retrieval, and speaker embeddings, the study constructs a rule-driven risk scoring system capable of identifying narrative reuse, structural inconsistencies, and cross-claim voiceprint repetitions. The framework demonstrates robust performance in component-level evaluations, significantly enhancing detection sensitivity while maintaining low false-positive rates, thereby establishing a new paradigm that transcends text-only fraud detection.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
multimodal fraud detection
synthetic dialogue generation
speaker diarisation
LLM-RAG
rule-based risk scoring
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