Cross-Checking Claimant Statements Across Multiple Claims: Automating Collusion Detection for SIU Investigators

Special Investigation Units (SIU) are stretched thin. Auto, Property & Homeowners, and General Liability & Construction claims continue to grow in volume and complexity, while suspected organized fraud and soft-collusion patterns hide in plain sight—recycled narratives, templated demand letters, and repeat actors who move between lines of business. Manually finding these signals across thousands of claim files, prior losses, and policyholders is slow, error‑prone, and often impossible under real-world deadlines.

Nomad Data’s Doc Chat for Insurance changes that. Built for high-stakes workflows, Doc Chat ingests full claim files—demand letters, claimant statements, prior claim files, settlement summaries, FNOL forms, ISO claim reports, police reports, medical records, repair estimates, coverage forms, endorsements—and enables SIU investigators to instantly search for similar claim narratives across policies, surface repeat claimants, and map provider/attorney networks. It delivers explainable answers with page-level citations so every red flag is auditable. In practice, this means days of manual cross-checking become minutes of targeted investigation.

The SIU Challenge: Collusion Patterns Hide in Unstructured Text

Fraud rarely announces itself. In Auto, Property & Homeowners, and General Liability & Construction, collusion frequently manifests as repeated language patterns, reused templates, and interconnected parties that appear across otherwise unrelated claims and policyholders. Examples include:

• Nearly identical claimant statements across different accidents, with the same phrasing about pain onset, mechanism of injury, or weather conditions.
• Copy‑and‑paste language in demand letters from repeat plaintiff firms, with identical descriptions, ICD‑10/CPT sequences, and settlement demands—even when the underlying incidents differ.
• Recurring clinics, DME suppliers, or body shops appearing across multiple claimant files, often with suspiciously uniform medical reports, repair estimates (e.g., CCC One), and billing patterns.
• Policyholders or third parties tied to a cluster of small losses over time—detected only by linking loss run reports, ISO claim reports, and prior claim files across the book.

Traditional keyword search cannot reliably detect semantic similarity or stylometric fingerprints. Two narratives that say the same thing with different words—or reuse very specific “stock” phrasing embedded deep within PDFs—will slip by. SIU investigators need a system that reads like a seasoned handler, links entities with high precision, and gives them instant, defensible evidence of collusion patterns across claims, policy years, and lines of business.

Nuances by Line of Business: Where Collusion Signals Often Emerge

Auto

In Auto, soft-tissue injury claims and staged accidents often feature recurring narratives and common medical/vendor networks. SIU investigators must parse through:

• Claimant statements, recorded statement transcripts, and EUO transcripts, looking for repeated phrasing (e.g., “sudden brake check,” “thrown forward,” “delayed onset” pain).
• Demand packages and demand letters, where the same law office recycles language across BI and PIP submissions.
• Medical records with repeated CPT/ICD-10 patterns, the same provider network, or near-identical treatment timelines, regardless of mechanism of injury.
• Police reports, repair estimates, and photo evidence, where damage patterns do not align with claimed injuries or the alleged collision.
• ISO claim reports and loss runs indicating repeat involvement of the same drivers, co-claimants, addresses, or vehicles across multiple carriers.

Property & Homeowners

In Property & Homeowners, collusion often appears as orchestrated contractor/public adjuster schemes or serial weather-related losses with uncanny narrative similarities. Watch for:

• Multiple hail or wind claims with the same FNOL forms language, identical “date of loss,” or templated origin descriptions across neighborhoods.
• Recurring contractors, public adjusters, or remediation firms in estimate files (e.g., Xactimate) with consistent over-scoping, unusual line items, or mirrored photographs.
• Settlement summaries and prior claim files revealing a pattern of small, frequent payouts clustered by service providers or adjusters.
• Origin-and-cause reports that echo prior narratives verbatim or conflict with weather logs.

General Liability & Construction

For GL & Construction, slip-and-fall rings and subcontractor collusion can involve repeat witnesses, similar incident descriptions, and the same treating providers. Evidence typically hides inside:

• Incident reports and claimant statements with strikingly similar phrasing across different insured locations.
• Demand letters and legal correspondence from the same counsel with boilerplate pain/suffering narratives and identical settlement structures.
• Recurring experts, clinics, or diagnostics providers tied to multiple claims with identical medical reports sections and billing patterns.
• Subcontractor agreements, COIs, and loss run reports revealing repeated overlap in parties, addresses, or bank accounts.

How It’s Handled Manually Today—and Why That Falls Short

Most SIU teams rely on manual search, ad hoc spreadsheets, and limited system queries. Even with good tools, the search surface is massive and unstructured.

• Open each claim file and read PDFs for unique phrases; try keyword searches that miss paraphrased narratives.
• Skim FNOL forms, ISO claim reports, loss run reports, and prior claim files to connect the dots—often across multiple systems and shared drives.
• Ask peers if they’ve “seen this language before,” or email Legal about a familiar demand letter style.
• Copy/paste text into search tools that can’t compare semantics, stylometry, or context across thousands of pages.
• Manually build entity lists (names, phones, VINs, addresses, providers, law firms), then reconcile spelling variants and OCR errors.

This approach strains timelines and attention. Investigators fight document sprawl, inconsistent formats, and sheer volume. Critical signals are easy to miss, and opportunities to triage faster or prevent leakage are lost. The result: delayed investigations, higher LAE, inconsistent decisions across desks, and increased litigation exposure.

Doc Chat Automates Cross-Claim Collusion Detection Without Adding Headcount

Doc Chat by Nomad Data was designed for precisely this challenge: AI for cross-claimant fraud at enterprise scale. It ingests entire claim files—including scanned PDFs and images—then normalizes, links, and continuously analyzes them for patterns. Unlike generic tools, Doc Chat is trained on your playbooks and document types, delivering consistent, audit-ready results.

What Doc Chat Does Differently

• Book-wide ingestion at speed. Doc Chat ingests thousands of pages per claim and scales across your entire book. Massive demand packages, medical records, police reports, ISO claim reports, and settlement summaries are analyzed in minutes, not days.
• Entity resolution and network mapping. It links people, addresses, phone numbers, VINs, providers, and law firms—even with misspellings, OCR noise, or format inconsistencies—to surface hidden connections.
• Semantic narrative comparison. Go beyond keywords. Doc Chat semantically compares claimant statements and demand letters across policies to find look‑alike narratives that humans would miss, enabling true collusion detection insurance claims workflows.
• Stylometric and template detection. Identify boilerplate language, repeated paragraph structures, and suspiciously consistent document phrasing across different claims or parties.
• Provider/attorney pattern discovery. Uncover clusters of repeat medical codes, treatment timelines, or settlement asks by the same clinics, DME suppliers, public adjusters, or firms across Auto, Property & Homeowners, and GL & Construction.
• Explainable AI with source citations. Every finding links back to the exact page and paragraph, ensuring SIU can defend decisions with evidence.
• Real-time Q&A. Ask: “Search for similar claim narratives across policies that mention a ‘sudden brake check’ within 30 days of the loss date,” or “List all demands over $50k by Firm X with identical injury language.” Answers come instantly with page-level references.

These capabilities reflect Nomad’s conviction that document processing isn’t simple scraping—it’s expert inference across messy, inconsistent materials. For a deeper dive into why inference matters, see Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

Exactly How Doc Chat Works for SIU

1) Normalize and Index Your Universe of Documents

Doc Chat ingests and normalizes PDFs, TIFFs, emails, images, and native documents across claims and policy admin systems. It reads:

• Claimant statements, EUO transcripts, FNOL forms
• Demand letters, legal correspondence, settlement summaries
• Prior claim files, ISO claim reports, loss run reports
• Medical reports, bills, CPT/ICD-10 codes, pharmacy and DME notes
• Police reports, accident diagrams, photos, repair estimates (CCC One), and property estimates (Xactimate)

2) Resolve Entities and Build Collusion Graphs

It reconciles variations in names, addresses, VINs, phone numbers, email domains, and provider/attorney names. The result is a graph of relationships across your book—who appears with whom, where, and how often. This is where hidden collusion networks emerge.

3) Compare Narratives with Semantics and Stylometry

Doc Chat analyzes conversation-level structures and linguistic fingerprints to detect recycled narratives—whether they are exact matches or sophisticated paraphrases. It flags unusually consistent phrasing in claimant statements, demand letters, and medical reports, and correlates them to providers, counsel, and loss types.

4) Trigger Intelligent Alerts and Triage

When the system detects repeating patterns or suspicious linkages, it routes the claim for SIU review with an evidence package: excerpts, citations, matched parties, and a concise narrative explaining the rationale. Adjust thresholds by line of business and claim severity to align with SIU capacity.

5) Answer Questions Like a Senior Investigator

With real-time Q&A, investigators can interrogate the entire corpus:

• “Show all demand letters in the past 12 months that describe ‘loss of consciousness’ with identical phrasing to Claim 22-0417.”
• “List all Auto BI claims within 20 miles that re-used the ‘sudden stop on wet road’ narrative and resulted in settlements over $25k.”
• “Find Property claims where the FNOL mentions ‘hail on 05/18 4:00pm’ and cross-check against NOAA weather data for that time.”
• “Map all claims involving Provider ABC Rehab and Attorney Smith & Partners, including ICD-10/CPT sequences and settlement summaries.”

This is AI for cross-claimant fraud in practice—fast, defensible, and tuned to your standards.

Business Impact: Time, Cost, Accuracy, and Leakage

When investigators can review whole claim files—and the entire book—in minutes, the impact is immediate:

• Cycle-time reduction: Triage and deep-dive investigations accelerate from days to minutes. One Nomad client described locating critical language in “seconds, not days,” which aligns with GAIG’s experience shared in Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI.
• Lower LAE: Less manual hunting and fewer outside vendor reviews for narrative comparison or discovery tasks.
• Accuracy and consistency: AI never tires at page 1,500. Findings are consistent, traceable, and linked to source pages—vital for internal audits, regulators, and litigated matters.
• Reduced leakage and better settlements: Reused narratives and repeat actors are surfaced early, strengthening negotiating positions and justifying targeted SIU actions.
• Scalability without hiring: Handle surges or multi-claim rings without adding headcount; automation absorbs peak loads.

These gains mirror broader Doc Chat results insurers have reported—processing that once took weeks now happens in minutes, with demonstrable quality improvements and measurable ROI. See the medical-file angle in The End of Medical File Review Bottlenecks, and operational ROI drivers in AI’s Untapped Goldmine: Automating Data Entry.

Real-World Scenarios Across Lines of Business

Auto: The “Brake-Check” Ring

A carrier’s SIU suspected staged rear-end collisions. Doc Chat ingested two years of Auto BI files, including claimant statements, demand letters, medical records, and settlement summaries. A semantic search for “sudden brake” narratives revealed clusters of claims describing identical motion, timing, and symptom onset—often treated at the same clinics, using near-identical CPT/ICD-10 sequences. Multiple claims shared the same law firm and DME supplier. With page-level citations and entity linkages, SIU escalated the matter, notified appropriate parties, and implemented targeted countermeasures during intake.

Property & Homeowners: Templated Hail Narratives

Doc Chat scanned Property files and found FNOL phrases like “hail at 4:00pm on 05/18,” repeated across neighborhoods with identical estimate structures and duplicated photo metadata. Cross-checks against weather logs (via custom connectors) showed no storm activity at the reported times. The system linked recurring contractors and public adjusters, enabling SIU to investigate a coordinated pattern. Prior claim files revealed similar pre-loss conditions, supporting a consistent investigative approach across policyholders.

GL & Construction: Recycled Slip-and-Fall Storylines

In GL claims, Doc Chat compared incident reports, claimant statements, and demand letters across multiple store locations and dates. It flagged suspiciously similar descriptions—identical adjectives, step counts, and lighting conditions—paired with the same law office and clinics. The SIU team validated these findings with citation links, escalated for EUOs, and used the evidence to guide defense strategy and settlement posture.

Why Keyword Search Fails—And Why AI Wins

Textual similarity in collusion doesn’t usually present as verbatim copy—it’s paraphrases, structure reuse, and stylometric fingerprints that persist even when the wording changes. Doc Chat’s semantic and stylometric analysis:

• Detects paraphrases that carry the same meaning with different wording.
• Surfaces identical structures across demand letters and medical reports even when sections are shuffled.
• Connects documents by shared providers, counsel, or co-claimants, even with spelling and formatting inconsistencies.

Doc Chat pairs this depth with explainability: every alert is backed by evidence and citations, so investigators can trust—and verify—the AI’s rationale. For a broader perspective on end-to-end claims automation and explainability, see Reimagining Claims Processing Through AI Transformation.

How SIU Teams Operationalize Doc Chat

Rolling out a collusion-detection program requires both technology and process discipline. Successful SIU teams build a lightweight operating framework around Doc Chat:

• Triage thresholds by LOB: Start with high-severity Auto BI and litigated GL claims; add Property weather losses next.
• Signals and weights: Reused narrative language, repeat providers, clustered CPT sequences, shared addresses/phones, and prior-loss overlap (via loss run reports and ISO claim reports).
• Governed review: SIU reviews Doc Chat alerts alongside page citations; outcomes feed back into the model’s routing rules.
• Feedback loop: Confirmed cases strengthen pattern signatures; false positives inform threshold tuning.
• Integration: Optional API integration can push alerts into your claim or SIU case system; or start immediately with drag‑and‑drop uploads.

Security, Auditability, and Responsible Use

SIU work involves sensitive PII and litigation-sensitive materials. Doc Chat operates with enterprise‑grade security and auditing, including page-level citations for every answer. Outputs are traceable to the source document and paragraph, providing defensibility for regulators, reinsurers, and courts. Nomad Data maintains modern compliance practices and is built to fit within your governance framework. Importantly, Doc Chat supports a “human-in-the-loop” model—AI highlights signals and patterns; SIU makes determinations.

Measurable Outcomes You Can Expect

Carriers typically see the following within the first 30–90 days of deployment:

• 50–90% reduction in time spent locating similar narratives and prior-loss connections.
• Material LAE savings by replacing manual cross-claim searches and narrowing outside vendor spend.
• Leakage reduction from earlier ring detection and stronger settlement posture.
• Higher SIU throughput without additional headcount—volume spikes become manageable.
• Defensible investigations via page-level citations and standardized evidentiary packages.

These improvements mirror the broader benefits our clients describe: consistent extraction, faster decisions, fewer blind spots, and happier teams who spend more time on high-value investigative work.

Why Nomad Data Is the Best Partner for SIU

Doc Chat isn’t generic summarization. It’s a suite of purpose‑built agents tuned to insurance, trained on your playbooks, and refined in collaboration with your SIU leaders.

• White‑glove onboarding: We interview your investigators, codify unwritten rules, and turn best practices into repeatable workflows that reflect your standards.
• Fast time to value (1–2 weeks): Start with drag‑and‑drop usage on day one; integrate via APIs as needed. Most teams see results within the first week.
• Volume and complexity: Process entire claim files—including 10,000+ page demand packages—with consistent accuracy and speed.
• Explainability and audit trails: Every answer links to source pages, enabling rapid verification and compliance alignment.
• Co-creation and evolution: As your SIU approach evolves, Doc Chat evolves with you—new red flags, providers, and templates are quickly incorporated.

If you’ve tried generic tools that felt like “web scraping for PDFs,” you’ll find Doc Chat fundamentally different. As we explain in Beyond Extraction, real insurance value comes from inference—connecting dots that aren’t written plainly on a page.

Frequently Asked SIU Questions

Can Doc Chat compare narrative similarity across the whole book—even when the wording is different?

Yes. Doc Chat’s semantic and stylometric models detect paraphrases and structural reuse across claimant statements, demand letters, and medical reports—far beyond keyword matching. That’s the core of effective collusion detection insurance claims work.

Will it connect to ISO claim reports and prior claim files?

Doc Chat reads and cross-references ISO claim reports, loss run reports, and prior claim files, normalizing parties and events. You can ask natural-language questions that blend these sources with current claim materials.

How does Doc Chat avoid false positives?

We incorporate thresholds, multi-signal corroboration (e.g., narrative similarity + shared providers + prior-loss overlap), and require page‑level citations. SIU remains in the loop for final decisions, ensuring the correct balance of sensitivity and precision.

What about security and regulatory expectations?

Doc Chat is built for regulated environments: robust security, explainable outputs, and complete traceability. Integration is optional; many SIU teams start with secure drag‑and‑drop and expand from there.

Getting Started: From Pilot to Standard Operating Procedure

Most SIU teams start small and scale quickly:

1. Identify a high-yield cohort (e.g., litigated Auto BI claims with large demand packages or Property weather losses with suspected templating).
2. Load representative claim files (claimant statements, demand letters, prior claim files, settlement summaries, ISO reports) and run a baseline analysis.
3. Validate hits with page citations; tune thresholds; align with counsel if needed.
4. Expand coverage across LOBs and add proactive triggers (e.g., triage alerts at FNOL or after demand receipt).
5. Integrate with claims/SIU case systems for seamless routing and reporting.

Because there’s no heavy IT lift to start, SIU leaders can prove impact in a matter of days and build momentum from real results.

The Bottom Line

Cross-claim collusion is a document problem at enterprise scale. Without AI, investigators are forced to triage with partial information and limited time. Doc Chat equips SIU with the ability to search for similar claim narratives across policies, link repeat actors, and build defensible cases—rapidly.

If your team is exploring AI for cross-claimant fraud or planning a broader transformation, start where impact is highest: narrative comparison across high-severity claims and repeat providers. Within weeks, you’ll see fewer blind spots, faster investigations, and stronger, evidence-backed outcomes.

Learn more about Doc Chat’s insurance capabilities and schedule a conversation: Doc Chat for Insurance.