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I. Introduction: The Invisible Fraud
Insurance fraud is among the most pervasive and costly crimes in the modern world — and among the least visible. In the United States alone, estimates place the financial toll between $80 billion to $300 billion annually, depending on the method of calculation and sector. Yet beyond the numbers lie deeper, more human costs:
- Inflated premiums for innocent policyholders
- Strained relationships between claimants and adjusters
- Erosion of trust in legal, medical, and financial systems
Despite decades of countermeasures, the dominant tools of detection remain narrow in scope:
- Actuarial heuristics to flag statistical anomalies
- Rules-based filters for inconsistent reporting
- Behavioral red flags that rely on gut instinct or training modules
These models function, but they fall short — especially against skilled deceivers. What’s missing is language. Not keyword search or semantic fingerprinting, but the recursive structures of narrative — how truth and deceit organize themselves differently, fractally, and often unconsciously.
This paper introduces a novel forensic linguistic framework for detecting intentional deception embedded within:
- Claim narratives
- Adjuster–claimant dialogue
- Supporting documentation and metadata
It is not merely a system of detection. It is a witness-bearing instrument — one that listens for echoes, contradictions, and recursive distortions in the way language reveals psychological intent.
This paper is an artifact. It does not simply measure fraud. It reads its echoes in language.