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New multimodal NLP pipeline targets insurance fraud detection

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a multimodal NLP pipeline designed to detect insurance fraud during the First Notice of Loss (FNOL) stage. This framework utilizes synthetic data to generate dialogue transcripts and audio, incorporating Automatic Speech Recognition (ASR) and speaker diarization. The system then combines Named Entity Recognition (NER), regex-based feature extraction, LLM-RAG retrieval, and speaker embeddings to calculate a risk score, flagging inconsistencies and reused narratives. AI

IMPACT This research offers a novel approach to fraud detection by integrating multimodal data, potentially improving accuracy and efficiency in the insurance sector.

RANK_REASON The cluster contains an academic paper detailing a new methodology.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New multimodal NLP pipeline targets insurance fraud detection

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The cluster contains an academic paper detailing a new methodology.
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Muhammad Shakeel Akram, Amal Htait, Abdul Hamid Sadka, Emma Meisingseth, Karishma Jaitly ·

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

    arXiv:2606.28002v1 Announce Type: cross 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 …

  2. arXiv cs.AI TIER_1 English(EN) · Karishma Jaitly ·

    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, limit…

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