Automated Red-Flag Triggers from Surveillance Reports and Field Investigator Notes for Workers Compensation and Auto SIU Investigators

Special Investigations Units (SIU) in Workers Compensation and Auto lines live in the gray areas—where statements, medical restrictions, and observable activity intersect. The challenge is not the lack of documentation but the overwhelming volume and inconsistency of it. Surveillance reports, investigator daily logs, IME reports, field correspondence, EUO transcripts, recorded statements, social media captures, and police crash reports all tell pieces of the story. The SIU Investigator’s job is to assemble those pieces quickly enough to influence coverage, reserves, and litigation strategy. That’s where Nomad Data’s Doc Chat changes the game.

Doc Chat by Nomad Data is a suite of purpose-built, AI-powered agents that performs end-to-end document review, contradiction analysis, claims summarization, intake and data extraction, and proactive fraud detection. It ingests entire claim files—thousands of pages at a time—and instantly surfaces red-flag triggers such as observed activity inconsistent with claimed injuries, contradictory statements between the claimant and witnesses, or vendor anomalies across multiple surveillance assignments. For SIU Investigators in Workers Compensation and Auto, Doc Chat turns days of manual review into minutes of defensible, source-linked intelligence.

Why this matters now

SIU teams are under pressure to move fast, justify referrals, and standardize red-flag criteria across desks. Meanwhile, surveillance vendors and field investigators deliver reports in wildly different formats and styles. Keyword searches and checkbox audits can’t keep up. Doc Chat brings structure to the chaos—finding the contradictions, flagging the suspicious activity, and showing precisely where in the file the evidence lives.

The Nuances of SIU Work in Workers Compensation and Auto

Workers Compensation and Auto SIU investigations rely on a delicate alignment of timelines, restrictions, statements, and observed behavior. Nuance is everything:

Workers Compensation SIU triggers often hinge on capacity, restrictions, and causation. Contradictions typically arise when surveillance reports or field investigator notes show:

Physical activity inconsistent with restrictions in IME reports, Functional Capacity Evaluations (FCEs), or treating physician notes (e.g., lifting heavy items, running, kneeling, climbing ladders).
Use (or non-use) of assistive devices in public that conflicts with clinic observations (e.g., no brace or cane during surveillance but required during PT sessions).
Employment or side work (e.g., seen performing cash jobs) while on Temporary Total Disability (TTD).
Timeline mismatches (e.g., observed driving to a gym during hours the claimant reported attending a medical appointment).
Pre-existing condition indicators or prior injury history hiding in old records that are contradicted by current statements.

Auto SIU triggers often center on mechanism of injury, property damage consistency, and patterns suggestive of build-ups or staged events. Common contradictions include:

Low property damage severity but high bodily injury claims; medical escalation not supported by crash data or injury mechanism.
Inconsistent EUO testimony versus prior recorded statements or police crash reports.
Common vendor, attorney, or treatment patterns across multiple claims suggesting potential ring activity.
Social media or surveillance activities (e.g., team sports, heavy lifting) inconsistent with claimed whiplash or back restrictions.
Referrals to the same cash-based providers, repetitive identical diagnoses or treatment narratives across different claimants.

Across both lines, SIU Investigators must reconcile content from a wide list of document types: surveillance reports and daily logs, field investigation correspondence, IME reports, EUO transcripts, recorded statements, medical records and billing (UB-04/HCFA 1500), wage statements, payroll records, employer HR files, police crash reports, photos, prior loss runs, FNOL forms, ISO claim reports, and social media captures. The complexity is compounded by data fragmentation—each source uses different terminology, structures, and date formats. Without automation, contradictory evidence hides in plain sight.

How This Work Is Handled Manually Today

SIU Investigators still do most of this work with elbow grease and spreadsheets. The manual process typically involves:

Linear reading and note-taking. Investigators read surveillance reports page by page, extract key statements, and try to map them against medical restrictions from IME reports or clinic notes. Different vendors’ formats make comparisons slow and error-prone.

Timeline stitching. Investigators manually compile activity timelines from surveillance logs, field investigator notes, appointment schedules, mileage vouchers, and PT calendars. Time-zone and timestamp inconsistencies create room for mistakes.

Keyword searches that miss nuance. Searching for “lift” or “running” won’t catch “carried boxes” or “loaded heavy coolers.” Important context is often embedded in narrative notes, side comments, or footnotes.

Copy-paste quoting and corroboration. SIU staff copy quotes into memos, then hunt for corroborating pages across IME reports, EUO transcripts, and prior recorded statements. Version control issues and multiple PDF batches slow everything down.

Training gaps. Much of what qualifies as a red flag lives only in senior investigators’ heads. New hires learn by osmosis, leading to inconsistent results and missed opportunities.

Back-and-forth with Claims and Counsel. By the time the SIU memo is ready, the file may have advanced. Opportunities to schedule EUOs, request addendums from IME providers, or extend surveillance windows can slip away.

The result is predictable: long cycle times, uneven quality, fatigue-driven errors, and inconsistent SIU referral and escalation decisions. Critical contradictions—like a claimant observed jogging the day before an IME that notes antalgic gait—get buried.

AI Analysis of Surveillance Notes for Insurance: How Doc Chat Automates the Hard Parts

Doc Chat was built for this exact problem: vast, inconsistent document sets where the answers are inferred—not just found. It automates SIU’s contradiction and red-flag analysis across Workers Compensation and Auto claims by:

1) Ingesting and normalizing everything

Doc Chat ingests entire claim files—surveillance reports, daily logs, IME reports, field investigation correspondence, EUO transcripts, recorded statements, medical records, police crash reports, FNOL forms, prior loss runs, ISO claim reports—thousands of pages at a time. It then normalizes formats, dates, and entities so that events line up across vendors and weeks of activity.

2) Building a verified timeline with citations

The system constructs a master chronology of observations, statements, restrictions, and appointments. Each entry is linked to its source page, paragraph, and timestamp, so investigators and counsel can verify instantly. This page-level explainability supports internal QA, regulators, reinsurers, and court scrutiny.

3) Contradiction detection and red-flag triggers

Doc Chat applies your playbook plus Nomad’s best practices to automatically surface contradictions and red flags. It highlights, for example, where an IME limits lifting to 5 lbs but surveillance shows repeated handling of 30-lb items, or where an EUO claim of no secondary income is contradicted by field notes indicating on-site work. It also detects cross-claim or cross-vendor patterns, such as repetitive diagnostic language or recurring clinics associated with build-ups.

4) Real-time Q&A across the entire file

Investigators ask plain-language questions and receive source-cited answers instantly: “List all observed activities inconsistent with the claimant’s knee restrictions,” “Compare EUO testimony to prior recorded statements about prior injuries,” “Summarize contradictions found in the last three surveillance reports,” or simply “find contradictions from investigations.” This capability makes Doc Chat the go-to system for AI analysis of surveillance notes insurance teams actually trust.

5) Presets tailored for SIU

With SIU-specific presets, Doc Chat produces standardized summaries titled “SIU Red-Flag Summary,” “Contradiction Matrix,” or “Surveillance vs. Restrictions Crosswalk,” ensuring every investigator produces a consistent, defensible deliverable. You define the format; Doc Chat fills it in—every time.

6) Scale and speed without added headcount

Doc Chat processes approximately 250,000 pages per minute, turning “review everything” from a backlog into a background task. Surge volumes—seasonal spikes, catastrophe events, or large surveillance drops—are handled automatically, with no overtime or temporary hires.

Learn why this is possible in Nomad’s article The End of Medical File Review Bottlenecks and how Doc Chat reads like domain experts in Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs.

What Doc Chat Flags: Activity Inconsistent with Injury Claims

Doc Chat operationalizes your SIU criteria with a deep library of recognized fraud indicators. It can be tuned to your Workers Compensation and Auto standards, then expanded as new patterns emerge.

Examples of triggers Doc Chat can surface and cite:

Activity inconsistent with restrictions: lifting/carrying loads above IME/FCE limits; jogging, squatting, climbing, kneeling; operating machinery allegedly contraindicated; extended driving despite claimed restricted mobility. This directly addresses the intent behind the search phrase “flag activity inconsistent with injury claim.”
Contradictory statements: differences between EUO and recorded statements; claimant vs. witness discrepancies; inconsistencies between clinic notes and patient-reported pain levels.
Timeline conflicts: surveillance showing activity during scheduled medical appointments; overlapping work shifts and PT sessions; gaps between injury date and FNOL without reasonable explanation.
Provider and billing anomalies: copy-paste diagnoses across unrelated claimants; identical narratives; unusual CPT/ICD combinations; cash-based providers with minimal documentation.
Pattern detection: repeated vendors, attorneys, or treatment routes across multiple claims; recurring addresses; common phone numbers; overlapping social networks that suggest ring activity.
Mechanism inconsistencies (Auto): injury severity inconsistent with property damage photos, crash reports, or event data recorder summaries.
Employment contradictions (Workers Compensation): field notes indicating off-books work while on TTD; surveillance showing job-related activity, tools, uniforms, or job site entries.

Every trigger includes source citations and a confidence rationale, streamlining escalation to EUO, IME addendums, or additional surveillance.

From Manual to Automated: A Day-in-the-Life Contrast

Manual SIU review: An investigator receives 600 pages of mixed documents for a Workers Compensation claim—surveillance reports from three vendors, an IME, PT progress notes, recorded statements, wage records, and employer HR emails. Over three days, they read, annotate, compile a timeline, and draft a memo. They find one contradiction and suspect a second but cannot validate before case conference.

With Doc Chat: The same 600 pages are uploaded. Within minutes, Doc Chat outputs an SIU Red-Flag Summary, a contradiction matrix aligning surveillance observations to IME restrictions, and a timestamped activity vs. appointment calendar. In under an hour, the investigator verifies two contradictions using page-cited evidence and requests an IME addendum and a targeted second-day surveillance window. Decisions land before the case conference, not after.

Business Impact for SIU Leaders in Workers Compensation and Auto

Doc Chat’s measurable impact shows up across cycle time, loss adjustment expense, leakage reduction, and morale.

Time savings: Reviews that take SIU Investigators 5–10 hours compress into minutes. For 10,000-page complex files, summaries and contradiction scans complete in under two minutes, as documented in our AI transformation overview.

Cost reduction: End-to-end automation trims manual touchpoints and reduces overtime. Surge capacity no longer requires temporary staffing. According to industry benchmarks discussed in AI’s Untapped Goldmine: Automating Data Entry, document automation regularly produces triple-digit ROI in the first year.

Accuracy and consistency: Machines don’t fatigue. Doc Chat reads page 1,500 with the same rigor as page 15. Page-level citations provide defensibility and facilitate rapid peer review, counsel review, and regulatory audits.

Leakage reduction and better outcomes: Earlier contradiction detection drives timely EUOs, targeted surveillance, IME addendums, and calibrated reserves. SIU referrals become more consistent, and litigation strategy improves with stronger documentary evidence.

Happier teams, lower turnover: Investigators spend less time on rote reading and more time on judgment calls, interviews, and strategy. This is echoed in real-world outcomes like those in Great American Insurance Group’s AI journey, where teams moved from scrolling to strategizing.

What Makes Nomad Data’s Doc Chat the Best SIU Solution

Not all AI is created equal. SIU is a high-stakes, high-complexity domain. Doc Chat wins on speed, quality, and partnership.

1) Built for volume and complexity: Doc Chat ingests entire claim files—including surveillance reports, investigation notes, IME reports, field investigator correspondence, EUOs, and more—and scales to portfolio-level audits with zero added headcount.

2) The Nomad Process: your playbook, codified: We train Doc Chat on your SIU rules, red-flag criteria, and preferred memo formats. This institutionalizes expertise, standardizes outputs, and preserves consistency across desks and geographies.

3) Real-time Q&A and explainability: Investigators ask natural-language questions and receive answer snippets with direct links to the source page and paragraph—ideal for counsel, compliance, and audit.

4) White glove onboarding in 1–2 weeks: You get value fast. We start with drag-and-drop file reviews, then integrate with claims and SIU systems via modern APIs. Most teams are fully operational in weeks, not months. See how we approach incremental rollout in our claims transformation article.

5) Security and compliance: Nomad Data maintains SOC 2 Type 2 certification. Data remains controlled, with page-level traceability for every answer. By design, foundation models do not train on your data by default—giving SIU leaders confidence to automate sensitive workflows.

6) A partner that evolves with you: With Doc Chat, you are gaining a strategic partner. We co-create red-flag libraries, tune contradiction logic, and roll out new presets for emerging schemes in Workers Compensation and Auto.

How SIU Teams Use Doc Chat in the Field

Standardized SIU Red-Flag Summary

Doc Chat outputs a preset SIU summary that your leaders define. Typical sections include:

Claim overview and alleged injury.
IME/FCE restrictions grid and effective dates.
Surveillance observations vs. restrictions matrix (with timestamps and citations).
Contradictory statements (EUO vs. recorded statements vs. medical histories).
Provider and billing anomalies (duplicate narrative, repetitive CPT/ICD patterns).
Mechanism consistency (Auto): BI claims vs. crash severity.
Recommendations: EUO topics, IME addendum prompts, targeted surveillance windows, OSINT follow-ups.

On-demand questions SIU Investigators ask Doc Chat

Because Doc Chat supports real-time Q&A, SIU Investigators use it as a daily co-pilot:

Examples

“List every observed activity that conflicts with knee flexion and lifting restrictions in the IME.”
“Compare the EUO testimony to the two prior recorded statements—summarize all inconsistencies about prior injuries and current work.”
“Show appointments the claimant reported attending during the hours surveillance observed them in the field.”
“Extract all references to employment, cash jobs, or tool handling from field notes.”
“Identify repeated diagnostic narratives across this claimant’s prior claims.”

These are plain-language versions of the high-intent searches SIU professionals already run internally—“AI analysis of surveillance notes insurance,” “find contradictions from investigations,” and “flag activity inconsistent with injury claim”—except now they return source-linked answers in seconds.

Implementation Blueprint: From Pilot to Portfolio in 1–2 Weeks

Week 1: Rapid pilot

Drag-and-drop upload of representative Workers Compensation and Auto SIU files (e.g., surveillance reports, investigation notes, IME reports, field investigator correspondence, EUO transcripts).
Define initial presets: “SIU Red-Flag Summary,” “Contradiction Matrix,” “Surveillance vs. Restrictions Crosswalk.”
Map your SIU red-flag checklist and thresholds (e.g., lifting over 25 lbs, kneeling/squatting duration, driving more than X miles).
Hands-on validation: Investigators ask live questions against familiar files and verify results using page-level citations.

Week 2: Operationalization

Refine presets and contradiction rules based on pilot feedback.
Enable intake automation via API or inbox-to-ingest routing for surveillance vendor reports and field investigator notes.
Integrate with claim and SIU case systems for document retrieval and submission of Doc Chat outputs.
Roll out to more investigators and finalize governance: audit trails, QA workflows, and escalation criteria.

Teams can start generating value the same day they see Doc Chat, and then move to full integration later, mirroring the approach discussed in our GAIG webinar replay.

Data Governance, Auditability, and Defensibility

SIU organizations require airtight chain-of-custody around insights. Doc Chat keeps a document-level audit trail and page-level citations for every conclusion. It shows exactly where a contradiction came from and why the system flagged it. This transparency builds trust with internal counsel, external defense panels, auditors, and regulators.

As described in Reimagining Claims Processing Through AI Transformation, human judgment remains central. Think of Doc Chat as a highly capable junior analyst—one that never tires, never forgets a page, and always cites its sources.

Expanding Your SIU Program with Portfolio-Level Insight

What happens when SIU can run red-flag triggers across an entire Workers Compensation or Auto portfolio in hours? Teams can:

Systematically identify providers, clinics, or vendors with anomalous patterns.
Cluster claims with overlapping addresses, phone numbers, attorneys, or treatment routes.
Quantify leakage risk tied to specific contradictions (e.g., activity restrictions violations linked to settlement uplifts).
Prioritize surveillance spend where the projected ROI is highest, based on early contradictions.

Portfolio analytics help SIU leaders defend budgets, redirect investigative spend, and forecast the impact of targeted programs on loss ratios and litigation rates.

Results You Can Expect

Based on Nomad Data’s client experience across complex claims and medical file review, SIU teams typically see:

50–90% reduction in manual review time per file; 2–5x more contradictions surfaced due to complete-file analysis; significant leakage reduction via earlier escalations; improved reserve accuracy and better settlement posture; and higher investigator satisfaction as work shifts from rote reading to strategic action.

These outcomes mirror those seen by claims organizations accelerating complex-file work with Doc Chat—documented in our medical file bottleneck and AI for Insurance use-cases articles.

Frequently Asked SIU Questions

Can Doc Chat handle mixed file types from multiple surveillance vendors? Yes. It reads PDFs, scanned images with OCR, and mixed-format uploads, normalizing data for consistent timelines and contradiction scans.

What about video? Many surveillance reports include still images, transcripts, or narrative logs. Doc Chat analyzes the documentation you receive and links red flags to the exact narrative or still-image timestamp in the report, ensuring defensibility.

How does Doc Chat avoid hallucinations? Doc Chat cites every answer to specific pages in your documents. Because it operates only on the materials you supply, answers are grounded and traceable.

Is our data used to train models? No, not by default. Nomad adheres to strict enterprise standards and SOC 2 Type 2 controls. Your data remains your data.

How quickly can we see value? Most SIU teams are live in 1–2 weeks. Many generate their first SIU Red-Flag Summary within hours of initial access.

Next Steps: Put Doc Chat to Work on Your Next SIU File

If your Workers Compensation or Auto SIU team spends hours combing surveillance reports and field investigator notes to find contradictions, it’s time to automate the heavy lifting. Let Doc Chat create your SIU Red-Flag Summary and Contradiction Matrix—fully cited, fully defensible—so you can move faster to EUOs, IME addendums, targeted surveillance, and settlement strategy.

See Doc Chat in action for SIU: https://www.nomad-data.com/doc-chat-insurance

Then explore how leading carriers eliminated complex-file bottlenecks and moved from scrolling to strategizing in our related resources:

With Doc Chat, SIU doesn’t just keep up—it gets ahead. Automate the review. Standardize the red flags. And bring evidence-backed speed to every Workers Compensation and Auto investigation.