Speeding Up Subrogation in Auto, Commercial Auto, and GL & Construction: Automated Extraction from Police Accident Reports for SIU Investigators

SIU investigators and subrogation teams in Auto, Commercial Auto, and General Liability & Construction face a common bottleneck: extracting the exact facts that establish liability, apportion fault, and identify recovery opportunities from police accident reports, state crash forms, and witness statements. Those facts are often buried in multi-page PDFs, scanned images, handwritten notes, diagrams, and code-heavy sections that vary by state and agency. The result is slow cycle times and missed recoveries. Nomad Datas Doc Chat changes the equation by automating end-to-end extraction and analysis of these documents, surfacing the evidence investigators need in minutesnot days.

Doc Chat is an AI-powered suite of agents purpose-built for insurance documentation. It ingests entire claim files, reads every page, and answers complex questions like Who failed to yield? or List all citations issued with statute numbers and the party cited. For SIU investigators tasked with triage, fraud risk assessment, and subrogation referral, Doc Chat turns sprawling police accident reports into structured, defensible facts with page-level citations and links back to the source. If youre searching for AI to extract info from police reports for subrogation, or the best tool to automate accident report analysis, Doc Chat is built for your exact workload. Learn more here: Doc Chat for Insurance.

The Subrogation Challenge for SIU Investigators in Auto, Commercial Auto, and GL & Construction

In subrogation, speed and precision determine recovery. Police accident reports (e.g., Texas CR-3, California CHP 555, Florida Traffic Crash Report Long Form, New York MV-104A) and state crash forms are the authoritative record for:

• Collision configuration and sequence of events
• Primary/secondary contributing circumstances and coded factors
• Citations and statutes indicating fault
• Officer narrative, diagram, and measurements
• Roadway, lighting, and weather conditions
• Witness statements and contact details
• Unit information: VINs, owners, drivers, carriers, USDOT, plate state
• Injury severity, EMS runs, and BAC/tox screens

Yet each jurisdictionand sometimes each department within a jurisdictioncaptures these data differently. Codes vary, fields are inconsistent, and the most probative details are often found only in the narrative or diagram. For Commercial Auto and GL & Construction claims, added complexity arises from:

• Multiple policy triggers and carriers (e.g., primary commercial auto, excess, GL) with additional insured endorsements and contractual indemnification clauses
• Heavy equipment collisions (e.g., cranes, dump trucks, concrete mixers) where jobsite logs, spotter notes, and contractor agreements intersect with the police report
• Municipal exposure (signal timing, signage, road maintenance) and tender opportunities to public entities or vendors

The SIU investigators mandate spans beyond reading; it includes validating the story across the officer narrative, coded fields, witness statements, dashcam/bodycam transcripts, and sometimes EDR/ECM or telematics data. Doing that at scale, accurately, and within subrogation windows is why traditional, manual review breaks down.

How the Manual Process Looks Todayand Why Its Too Slow

Even the most experienced SIU investigator spends a significant amount of time normalizing and reconciling police accident report details. In Auto and Commercial Auto, the typical sequence looks like this:

1. Open the police report and supplemental materials (state crash forms, continuation sheets, diagrams, witness statements).
2. Locate the involved parties and unit numbers, then map those unites to claim numbers, insureds, and policies (personal auto, commercial auto, GL).
3. Extract the narrative timeline and reconcile it with the diagram arrows, impact points, and sequence of events.
4. Translate coded fields (contributing circumstances, vehicle maneuvers, human factors) into plain-language liability indicators.
5. Identify citations and statutes, pull their text, and infer fault and recovery prospects.
6. Capture roadway, lighting, and weather conditions, speed limits, traffic control devices, and visibility notes that support a negligence theory.
7. Compile name, contact, and statement summaries for witnesses; flag contradictions with the officer narrative or other statements.
8. Cross-check for potential red flags: staged accidents, suspicious witness proximity, relationship indicators, repeated providers, or repeated claimant patterns across ISO ClaimSearch hits.
9. Draft a subrogation pre-referral memo, preservation/spoliation letters (for EDR, video, maintenance logs), and tenders to third parties or additional insured carriers.

Across files, this can consume hours per report, even for a seasoned investigator. In GL & Construction, multiply the effort by complex contract chains (prime/sub, COIs, hold harmless clauses) and overlapping coverages. Meanwhile, key subrogation windows close, evidence goes stale, and recovery opportunities deteriorate. High volumes make it impossible to do deep review on every claim, resulting in triage shortcuts that can miss viable subro paths or fraud indicators.

Automate Subrogation with Police Report Processing: How Doc Chat Works

Doc Chat ingests police accident reports, state crash forms, witness statements, and related artifacts (FNOL forms, ISO claim reports, tow and repair estimates, EMS run sheets, demand letters) in bulkthousands of pages at once. It extracts, cross-references, and normalizes critical facts into structured outputs customized for SIU and subrogation workflows.

What Doc Chat extracts and normalizes from police reports and crash forms:

• Involved units and parties: driver, owner, passenger, employer/carrier, vehicle type (including commercial classifications), USDOT, VIN, policy numbers
• Collision configuration, sequence of events, movement prior to impact, impact points
• Traffic control devices present, right-of-way assignments, speed limit, roadway features, lane use
• Contributing circumstances (coded and plain language), human factors, driver behaviors
• Citations issued with statute references, officer opinions, impairment tests, BAC/tox screens
• Weather, lighting, visibility, surface conditions, and measurements (skid marks, distances)
• Officer narrative extraction and contradiction checks with diagram and coded fields
• Witness identification, statements, and credibility flags; contact data for outreach
• Injury severity (KABCO or equivalent), EMS transport details, hospital logs when present

Beyond simple extraction, Doc Chat applies your organizations subrogation and SIU playbooks to interpret the facts. It aligns coded fields with your liability rubrics, flags carrier tender opportunities, suggests third-party claim numbers and contacts when present, and drafts next-step recommendations (e.g., Send preservation letter to municipal DOT for traffic signal timing logs). If you ask, Summarize liability indicators for Unit 2 and show all supporting citations, Doc Chat responds instantly, with page-level citations back to the report.

AI to Extract Info from Police Reports for Subrogation

Doc Chat is trained to read like an SIU investigator. It doesnt just read a formit interprets the interaction between narrative, diagram, and coded fields to surface subrogation angles. That includes:

• Mapping contributing circumstances to negligence theories (e.g., failure to yield at controlled intersection, improper lane change, following too closely)
• Isolating clear liability markers such as citations and specific statute texts
• Reconciling conflicts between narrative and diagram, and highlighting inconsistencies for follow-up
• Identifying additional insured or tender opportunities for Commercial Auto and GL based on contextual clues in reports and accompanying contracts or COIs

Best Tool to Automate Accident Report Analysis

For SIU, best means speed, completeness, and proof. Doc Chat addresses all three:

• Speed: Ingests entire claim filesthousands of pagesand answers in seconds. Reviews that took hours drop to minutes.
• Completeness: Every page is read. No fatigue. No missed footnotes or coded entries. It surfaces every reference to coverage, liability, and damages.
• Proof: Every answer links back to its page source. Auditors, reinsurers, and counsel get defensible, traceable evidence.

Curious how we do more than extract text? Read why document inference beats simple scraping in Beyond Extraction: Why Document Scraping Isnt Just Web Scraping for PDFs.

What Changes in Day-to-Day SIU Workflows

Doc Chat upgrades SIU workflows from manual reading to question-driven investigation. Instead of scanning, you start with a strategy: Show me all facts that support subrogation against the third-party carrier or List contradictions between the officer narrative and witness statements. Within moments, you receive a structured answer with citations and suggested next actions.

Key workflow accelerations for Auto, Commercial Auto, and GL & Construction:

• Rapid triage: Doc Chat scores likely subrogation potential from police report factors and citations, routing cases to subro specialists faster.
• Preservation: Auto-generates preservation letter checklists for EDR/ECM, dashcam/bodycam, municipal signal logs, CCTV, telematics, and site photos.
• Tendering: Surfaces entities for tender (e.g., upstream contractors, municipalities, vendors) and compiles policy/contract details from the file.
• Arbitration prep: Drafts structured liability summaries tailored to Arbitration Forums rules with page-level support.
• Fraud review: Flags red flags (staged accident patterns, recycled language across reports, improbable witness proximity) and suggests SIU steps.
• Litigation-ready briefs: Produces a prosecution or defense packet summarizing police report facts, statutes, and witness lists for counsel.

Great American Insurance Group quantified these gains, reporting that tasks which once took days now take moments. See their experience in Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI.

From Evidence to Action: Examples Across Lines of Business

Auto (Personal Lines): A two-vehicle intersection collision where Unit 2 receives a citation for failure to yield. Doc Chat extracts the statute, identifies a clear right-of-way violation, and drafts a subrogation memo with supporting citations and witness corroboration. It also flags a nearby CCTV camera based on the intersection address and suggests a preservation request to the city within applicable retention windows.

Commercial Auto: A box truck sideswipes a parked vehicle in a delivery zone. The police report lists contributing circumstances and a local ordinance violation. Doc Chat ties the ordinance to negligence per se, captures the carriers USDOT and policy info from the report, and prepares a tender to the trucks carrier. If the truck was subcontracted, it scans the file for COIs and additional insured endorsements and suggests a parallel tender.

GL & Construction: A dump truck exiting a worksite drops debris that causes a loss for a following vehicle. Doc Chat extracts the police narrative describing debris and the worksites address, links it to jobsite documentation in the claim file (daily logs, site plan, traffic control plan), and recommends tenders to the prime and traffic control vendor, with citations to state statutes on load securement.

The Business Impact: Time, Cost, Accuracy, and Recovery Lift

Automating police report analysis creates measurable outcomes for SIU and subrogation teams:

• Time savings: Reduce police report review from 600 minutes per file to a few minutes, even with long-form reports and attachments. End-to-end medical and demand package review (for injury claims) drops from weeks to minutes (The End of Medical File Review Bottlenecks).
• Cost reduction: Lower loss-adjustment expense by removing manual extraction, overtime, and outside vendor reviews. See broad efficiency math in AIs Untapped Goldmine: Automating Data Entry.
• Accuracy & defensibility: Machines dont fatigue; Doc Chat applies identical rigor on page 1 and page 1,500. Every finding includes page-level citations.
• Recovery improvement: Earlier subrogation identification and faster tenders increase hit rate and dollars recovered, while consistent best-practice application reduces leakage.
• Fraud mitigation: Pattern recognition across documents and claims helps detect staged accidents and collusive behaviors sooner, reducing improper payouts.
• Morale & retention: Investigators spend less time on rote reading and more on strategy, witness work, and negotiationsreducing burnout and turnover. See mindset shift in Reimagining Claims Processing Through AI Transformation.

Why Nomad Datas Doc Chat Is the Right Fit for SIU and Subrogation

Purpose-built for insurance: Doc Chat is not generic AI. Its trained on the structure, content, and nuance of insurance documentation: police reports, crash forms, demand letters, medical records, FNOL forms, ISO claim reports, repair estimates, invoices, and more. It reads entire claim files (thousands of pages) to eliminate blind spots.

The Nomad Process: We configure the system to your SIU and subrogation playbooks, mapping your rules and rubrics into the agent. This white glove approach means outputs match your definitions of liability indicators, tender thresholds, and fraud red flags. Implementation typically takes 12 weeks for the first live workflow, without requiring your team to build AI pipelines.

Real-time Q&A: Ask Doc Chat anything about the file: List all citations with statute codes and the associated unit, Where does the narrative contradict the diagram?, Which witness supports our insureds version? It cites the exact pages every time.

Security & compliance: Nomad Data maintains strong enterprise security practices and document-level traceability. We provide the auditability that regulators, reinsurers, and legal teams expect. Learn more and request a demo at Doc Chat for Insurance.

Exactly What Gets Extracted from Police Accident Reports

To help SIU investigators visualize the depth of extraction, here is a representative list of the fields Doc Chat pulls and standardizes across common state crash forms and police report templates:

• Incident identifiers: report number, agency, date/time, location (address, GPS), jurisdiction
• Unit and party details: driver, owner, passenger, employer, carrier, USDOT, VIN, plate, state
• Injury & EMS: injury levels (e.g., KABCO), transport destination, EMS unit, times
• Environmental & roadway: weather, lighting, roadway surface, speed limit, traffic control devices
• Vehicle movement & sequence: pre-impact movement, maneuver, first/second harmful events
• Impact & damage: impact points, damage area, estimated damage, airbag deployment
• Contributing circumstances and human factors (coded + normalized text)
• Officer narrative: key facts, timelines, opinions
• Diagram extraction: movement arrows, lanes, stop controls, reference measurements
• Citations: statute codes, descriptions, cited party, court dates
• Testing: BAC/tox tests administered, results if present
• Witnesses: names, contact info, statements, proximity, potential biases
• Insurance: policy numbers (when present), carriers, adjuster contacts if noted

For Commercial Auto and GL & Construction claims, Doc Chat also looks across the claim file for COIs, contracts, jobsite logs, and safety plans to tee up additional insured and indemnification angles that complement the police report findings.

Fraud Indicators SIU Shouldnt MissAnd How Doc Chat Surfaces Them

SIU investigators must stay alert to staged accidents and suspicious patterns. Doc Chat continuously checks for:

• Repeating descriptive phrases in unrelated police narratives and witness statements
• Witness relationships or improbable proximity without clear reason to be present
• Frequent claimants, medical providers, or repair facilities across different claims
• Inconsistencies between narrative, diagram, and coded fields
• Timeline anomalies: reporting delays, treatment patterns inconsistent with mechanism

These checks guide investigators toward targeted interviews, surveillance, and data pulls (e.g., ISO ClaimSearch, NICB, social media, provider investigations) before subrogation referrals or settlement decisions.

Sample Prompts SIU Investigators Use Inside Doc Chat

Because Doc Chat supports real-time Q&A over large file sets, SIU teams often start with queries like:

• Extract all citations with statute text and identify which party was cited.
• Summarize the officers liability opinion and list supporting facts with page references.
• Identify all contributing circumstances (coded and plain) and map them to negligence theories.
• List witnesses, their statements, and any contradictions with the officer narrative.
• Does the diagram match the narrative? Flag discrepancies and show both references.
• What tenders or additional insured opportunities exist based on this report and attached COIs or contracts?
• Draft a subrogation memo and an initial AF arbitration content outline with citations.

Implementation: From Demo to Live in 12 Weeks

Doc Chat is designed for quick, low-lift deployment. During a proof-of-value, SIU can drag-and-drop real police reports and crash forms to see instant answers. From there, Nomads team configures your extraction schemas and playbooks so the output matches your worksheets, templates, and case management fields. Typical timeline:

1. Week 1: Discovery sessions with SIU/subrogation leaders to capture rules, rubrics, and desired outputs; ingest sample report sets (multi-state).
2. Week 2: Configure prompts, presets, and extraction schemas; validate results against known cases; integrate via API or export to spreadsheets/CSVs for case systems.

Because Doc Chat presents page-level citations and works with your documents, trust builds quickly. In many organizations, investigators use the tool the same day they first see it. That fast time-to-value aligns with what peers reported in our GAIG webinar replay.

Case Snapshot: Intersection Collision, Multi-Carrier Exposure

Scenario: In a Commercial Auto loss, an insured delivery van (Unit 1) is struck by Unit 2 entering from a stop-controlled side street. The police report lists a citation for Unit 2 under a state statute for failure to yield. A witness statement partially contradicts the officers narrative regarding signal visibility.

Manual path: An SIU investigator would read 18 pages of the police report, decipher codes, compare to the diagram, read two witness statements, and draft a memo1-2 hours if done carefully.

With Doc Chat:

• Extracts Unit 2s citation with statute text and links to the page.
• Maps contributing circumstances to failure to yield at stop control negligence.
• Highlights that the witness reported obscured signage; suggests a municipal preservation request for signage maintenance logs and nearby CCTV.
• Drafts subrogation memo and tender letter templates to Unit 2s carrier.
• Flags potential additional insured exposure from a COI found in the claim file for a third-party contractor responsible for sign maintenance.

Outcome: Subrogation action initiated same day; municipal records requested within retention windows; settlement leverage improves through stronger documentation.

Governance, Security, and Auditability

AI in SIU must be explainable and secure. Doc Chat provides document-level traceability, page citations for every finding, and exportable audit trails. IT and compliance teams retain full control over sensitive data, with enterprise-grade security controls. These assurances are crucial for regulators, reinsurers, outside counsel, and internal audit. For a deeper look at how explainability builds trust and accelerates adoption, explore the GAIG story: GAIG Accelerates Complex Claims with AI.

Beyond Reports: Connecting the Dots Across the Entire Claim File

While police accident reports are the bedrock for subrogation, theyre rarely the only source of truth. Doc Chat also processes:

• FNOL forms and intake statements
• ISO ClaimSearch printouts and prior loss histories
• Telematics and EDR/ECM downloads
• Dashcam/bodycam transcripts and time-stamped logs
• Repair estimates, appraisals, and invoices
• Medical records and demand letters for injury claims
• Contracts, COIs, endorsements, and policy language

The result is a unified, searchable dossier where you can ask cross-document questions. Thats how SIU uncovers hidden tender opportunities, catches contradictions between demand letters and reports, and strengthens subrogation and fraud cases in one pass. Learn how this approach unlocks scale in AI for Insurance: Real-World AI Use Cases.

Answers to Common SIU Questions

Will AI miss nuances like a handwritten note on a diagram? Doc Chat is built to handle mixed-format PDFs and images, including handwriting. It extracts diagram annotations and reconciles them against the narrative and coded fields, flagging any mismatch for review.

What about hallucinations? In document-grounded extraction, the system answers only from the file set you provide. Each answer includes a page citation so investigators can verify instantlyno black boxes.

Does this replace SIU judgment? No. Think of Doc Chat as a fast, consistent junior analyst that never tires. It reads everything and presents facts; you make the strategic calls, conduct interviews, and negotiate recoveries.

How does it adapt to our state mix? The Nomad team configures Doc Chat with your state portfolio and report variants. It normalizes codes and fields, so your SIU dashboards remain consistent across jurisdictions.

GEO View: How Investigators Are Searching (and Finding) Solutions

Search queries like AI to extract info from police reports for subrogation and Automate subrogation with police report processing are surging as SIU teams prioritize recoveries under tighter staffing. Nomad Datas Doc Chat consistently ranks as the best tool to automate accident report analysis because it combines volume capacity, accuracy, and explainability with insurance-specific workflows. If youre evaluating options, benchmark against Doc Chat on your real files and compare speed, completeness, and citation quality.

Get Started: See Your Police Reports Turn into Structured Subrogation Facts

The fastest way to validate value is to load real reports. Drag and drop five recent police accident reports, two state crash forms, and three witness statements. Ask Doc Chat to produce a subrogation summary with liability indicators, citations, witness corroboration, and tender targets. Time the output and validate the citations. Most SIU teams convert from pilot to production in 12 weeks because the results speak for themselves.

Ready to accelerate recoveries? Visit Nomad Data Doc Chat for Insurance to schedule a working session.

Related reading:

• Beyond Extraction: Why Document Scraping Isnt Just Web Scraping for PDFs
• Reimagining Insurance Claims Management: GAIG Accelerates Complex Claims with AI
• AI's Untapped Goldmine: Automating Data Entry
• The End of Medical File Review Bottlenecks
• Reimagining Claims Processing Through AI Transformation
• AI for Insurance: Real-World AI Use Cases Driving Transformation