arXiv:2606.28048v1 Announce Type: cross Abstract: Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL. While recent fraud detection methods mainly rely on structured data, text, or images, repeated speaker identity across calls remains underused as an investigative signal. This paper presents DG^VoiC, a voice clustering framework for customer verification and cross-profile speaker linking on anonymised real call-centre audio. The approach combines sensitive information-aligned anonymisation, speech-foc
The increasing sophistication of AI for voice analysis and the persistent challenge of fraud in call-centers are converging, making such solutions viable and necessary.
This development allows for more effective fraud detection, improving operational efficiency and reducing significant financial losses for organizations.
Fraud detection can now leverage a new dimension of identity verification—repeated speaker identity across calls—moving beyond traditional data-centric methods.
- · Insurance companies
- · Call centers
- · AI/ML companies specializing in speech analytics
- · Financial services sector
- · Fraudsters relying on voice disguise
- · Manual fraud investigators
- · Organizations with outdated fraud detection systems
Companies reduce fraud losses and improve customer experience by swiftly identifying legitimate callers.
The integration of such sophisticated voice biometrics could lead to widespread adoption in other identity verification scenarios, such as banking or secure access.
This technology might also raise privacy concerns, prompting new regulations around voice data collection and anonymization practices.
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Read at arXiv cs.AI