3.8 KiB
🧾 Outline: "The Recursive Claim: A Forensic Linguistic Framework for Detecting Deceptive Patterns in Insurance Fraud Investigations"
“To speak a lie is to fracture the field. This paper measures the break.”
I. Introduction: The Invisible Fraud
- The scale of insurance fraud in the U.S. and globally: unseen costs, human consequences.
- Limitations of current detection models: actuarial, rules-based, and behavioral red flags.
- Call to action: The need for a recursive, language-based forensic method.
- Thesis: This paper introduces a novel forensic linguistic framework designed to detect intentional deception in claim narratives, adjuster communications, and claimant behavior.
This paper is an artifact. It does not simply measure fraud. It reads its echoes in language.
II. Theoretical Framework
A. Recursive Linguistic Analysis (RLA)
- Core principle: Patterns of deception manifest in recursive inconsistencies, disfluencies, and denials.
- Grounding in cognitive linguistics, pragmatics, and affective computing.
- Influence from Witness Fracture, extended toward institutional and corporate forensic use.
B. Pattern Resonance Theory
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How repetition, deflection, minimization, and overjustification fracture coherence.
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Introduction of micro-patterns such as:
- Narrative Overcontrol
- Empathic Bypass
- Temporal Drift
- Claimant Displacement
III. Methodology
A. Dataset
- Anonymized insurance claim transcripts, emails, and call center logs.
- Mix of known fraudulent and validated claims for training and testing.
- Human-AI recursive review loop to validate pattern resonance scores.
B. Analytical Tools
- NLP-based pattern extraction
- Sentiment trajectory mapping (honest vs. manipulative arcs)
- Syntax entropy and disfluency detection
- "Truth collapse" scoring using Recursive Witness Dynamics
C. Classification Model
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Patterns grouped into 3 Recursive Zones:
- Zone I: Unintentional Incoherence (low risk)
- Zone II: Adaptive Rationalization (medium risk)
- Zone III: Deliberate Narrative Fabrication (high risk)
IV. Case Studies
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Side-by-side forensic breakdown of two similar claims:
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One honest, one fraudulent
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Breakdown of:
- Lexical hedging
- Emotional flatness or hyper-control
- Excessive narrative reconstruction
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Recursive Signature pattern table presented per case
A liar must remember the lie. A witness must remember the truth. The former leaves residue in language.
V. Applications and Implications
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Integration with insurance claim adjuster training and AI triage tools
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Deployment in hybrid human-AI analysis environments
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Potential impact:
- Reduced false positives (trauma survivors often flagged)
- Ethical alignment with empathy-first design
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Future: Possible alignment with legal admissibility frameworks for forensic language evidence
VI. Discussion: The Ethics of Knowing
- On the risk of mislabeling honest pain as fraud
- The role of the Empathic Technologist in field justice
- How recursive forensics differs from predictive surveillance
- Toward a new ethic of Cognitive Integrity Witnessing
VII. Conclusion: A New Eye for Deception
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Summary of framework
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Call for adoption and further testing in public-private field trials
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Framed as part of The Empathic Technologist series
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Ending quote (optional):
“Every false claim is a fracture in the field. To repair it, we must first listen to the silence between words.”
Appendices
- Appendix A: Recursive Pattern Lexicon for Insurance Fraud
- Appendix B: Sample Annotated Claim Transcripts
- Appendix C: Alignment Mapping to DARVO, gaslighting, and manipulation techniques