Speeding Up Subrogation: Automated Extraction from Police Accident Reports — Auto, Commercial Auto, General Liability & Construction

Auto claims adjusters live in the details: unit numbers, contributing factors, citations, diagrams, witness contact info, road conditions, and narratives buried inside police accident reports and state crash forms. Those details determine liability and drive subrogation. The challenge? Every jurisdiction uses different forms and codes, police narratives vary widely, and claims teams face relentless volume and time pressure.

Nomad Data’s Doc Chat eliminates this bottleneck. Purpose‑built for insurance, Doc Chat automatically ingests police accident reports, state crash forms, and witness statements, then extracts all subrogation‑relevant facts in minutes. It summarizes liability indicators, normalizes contributing factor codes across states, flags third‑party carriers, and creates a defensible, cited record you can trust. For auto claims adjusters across Auto, Commercial Auto, and General Liability & Construction, Doc Chat turns slow, error‑prone report review into instant, actionable intelligence.

Why police accident reports are so hard to process at scale

In Auto and Commercial Auto, subrogation hinges on precise reconstruction of the loss: who had right of way, which driver was cited, where impact occurred, what traffic controls were present, whether impairment or distraction played a role, and how weather or road conditions contributed. In General Liability & Construction, the downstream questions often expand: was there a roadway work zone, who controlled the site, were there third‑party contractors or municipalities involved, and do contractual indemnity or additional insured endorsements shift responsibility?

The nuance intensifies because police accident reports differ dramatically by state and even by municipality. An auto claims adjuster might see in one week:

Texas CR‑3 (TxDOT) crash reports with unit, person, and contributing factor codes
California CHP 555 crash reports with diagrammatic annotations and party/vehicle boxes
New York NYPD MV‑104A and supporting MV‑104 driver statements
Florida HSMV 90010S Long/Short Form crash reports with citation references
Illinois Traffic Crash Report and supplemental narratives

Across these forms, critical fields are named differently, appear in different sections, or live only in the narrative. Officers use abbreviations or local coding tables. Scans can be low quality. Witness statements and supplemental pages arrive as separate PDFs. The result: adjusters spend hours per claim hunting for the same facts, often under strict cycle-time goals.

How the manual process works today — and where it breaks

Most auto claims adjusters and subrogation units follow a familiar manual playbook. After FNOL and initial liability triage, they receive the police accident report and any available state crash forms or witness statements. They read the entire packet, highlight key elements, and start building a liability worksheet. The process typically includes:

Reading the narrative line by line to identify who was at fault, any citations, and contributory behaviors (speed, distraction, failure to yield)
Decoding form-specific contributing factor and sequence-of-events codes
Extracting unit numbers, VINs, plate states, and driver IDs to match to the claim file
Mapping the diagram to impact points and lane positions to validate consistency with photos and repair estimates
Recording traffic control devices, weather, light conditions, and road surface conditions
Capturing witness names, phone numbers, and statements to prioritize outreach
Checking for impairment testing, HAZMAT notes, or commercial carrier indicators (USDOT, MC numbers)
Identifying the other carrier and policy number (when listed) to start subrogation recovery

In practice, this manual workflow is fragile. Volumes spike, time evaporates, and fatigue increases error risk. New adjusters may miss hidden indicators (e.g., a contributing factor buried in the continuation page, or a diagram arrow that contradicts the stated narrative). Commercial Auto and GL & Construction add document complexity: motor carrier filings, MCS‑90 endorsements, jobsite incident reports, municipal permits, and hold‑harmless agreements. Each new document type is another place where liability-shifting facts can hide.

AI to extract info from police reports for subrogation: how Doc Chat solves the problem

Doc Chat is a suite of AI-powered agents designed for unstructured insurance documentation. It ingests entire claim files — including police accident reports, state crash forms, witness statements, FNOLs, demand letters, and correspondence — and returns structured, defensible answers. For subrogation and liability assessment, Doc Chat focuses on:

Structured data extraction: Unit numbers, driver identities, VINs, plate numbers, vehicle make/model, policy details, road conditions, traffic controls, and contributing factor codes are extracted and normalized to your data model.
Narrative and diagram inference: The AI correlates diagram arrows, impact points, and lane markings with the written narrative and photos to validate consistency.
Liability indicators: Citations, right-of-way references, stop-sign or signal control, lane-change violations, hazard presence, and admission statements are surfaced with page-level citations.
Witness triage: Names, contact information, and priority score (based on proximity, observation clarity, and corroboration with the narrative) are extracted to accelerate outreach.
Third-party carrier detection: When a carrier/policy is named in the report, Doc Chat flags the insurer, policy number, and contact if present.
Comparative negligence mapping: The system highlights facts that commonly drive adjusted fault splits in your states, supporting defensible apportionment decisions.

Critically, Doc Chat answers plain-language questions in real time. Ask: “List all citations issued and the corresponding drivers,” “Show the reported speed and posted speed for each unit,” “Who had the right-of-way per the officer’s determination?” or “Create a subrogation facts summary with all page citations.” Answers arrive instantly — even across thousands of pages — and always link back to the exact source pages for rapid verification.

Best tool to automate accident report analysis: what makes Doc Chat different

Many tools can OCR a police report. Very few can read like an experienced auto claims adjuster. Doc Chat was built for insurance document inference, not just extraction. As we describe in our piece Beyond Extraction: Why Document Scraping Isn’t Just Web Scraping for PDFs, the hard part is turning scattered clues into operational decisions. Doc Chat learns your subrogation playbooks, your comparative negligence heuristics by state, and your filing standards — then applies them consistently to every file.

Another differentiator is transparency. Every answer includes a link to the source page and coordinates where the fact was found, mirroring the page-level explainability highlighted in our case study with Great American Insurance Group: Reimagining Insurance Claims Management. That means your adjusters and subrogation specialists can trust the output, your QA team can audit it, and your counsel can defend it.

Automate subrogation with police report processing: from intake to recovery

With Doc Chat, Auto, Commercial Auto, and GL & Construction teams automate the entire front end of subrogation — and a good portion of the back end. Here’s what that looks like:

Intake and classification: Drag‑and‑drop a police report PDF, state crash form, or witness statement. Doc Chat identifies the form type (e.g., TX CR‑3, CA CHP 555, NY MV‑104A, FL HSMV 90010S) and routes it to the correct extraction pipeline.
Extraction and normalization: All key fields are captured, mapped to claim system fields, and normalized (e.g., converting state‑specific factor codes to your consistent taxonomy).
Liability summary and subro signal: Doc Chat creates a subrogation facts summary, flags probable recovery potential, and proposes a preliminary comparative negligence split based on your guidelines.
Third‑party outreach: The AI drafts subrogation notice letters with cited facts and attaches the relevant report pages. It can also populate your workflow for outbound carrier contact.
Ongoing Q&A: Adjusters ask follow-up questions at any time — “Confirm if the commercial unit displayed a USDOT number,” “List all references to construction zones,” “Did any witness contradict the insured’s statement?” — and receive instant, sourced answers.

The nuances across Auto, Commercial Auto, and General Liability & Construction

Personal Auto subrogation hinges on clear right‑of‑way determinations, signal control, yield/turning violations, rear‑end presumption (as applicable), and corroborating citations or admissions. Officers often encode these facts in checkboxes and continuation narratives. Doc Chat reads both, cross‑checks them against photos, and aligns them with your state’s fault rules.

Commercial Auto adds layers: CDL status, hours‑of‑service implications (if referenced), hazardous materials notes, placards, and indications of motor carrier status (USDOT/MC references in the report or on-vehicle). Police reports sometimes include the carrier name or policy numbers, or at least a lead to the fleet. Doc Chat elevates these details, supporting accelerated identification of a recovery target. It can also surface mentions of vehicle class (e.g., combination vehicles) that may affect damage severity and negotiation strategy.

General Liability & Construction claims tied to roadway projects or jobsite traffic control raise different questions: Was a contractor responsible for temporary signage? Did a municipality issue a permit with specific requirements? Were there lane closures noted in the report? Doc Chat flags notices of construction zones, detours, or barricades in the police narrative and correlates those references with your contract file (e.g., hold‑harmless clauses, additional insured endorsements). That can change the recovery posture from pure Auto to GL/Construction, or from third‑party driver to contractor or public entity.

What adjusters typically need from police reports (and how Doc Chat delivers it)

Regardless of line of business, the subrogation‑ready facts are remarkably consistent. Doc Chat produces a standardized, audit‑ready output that includes, at a minimum:

Crash date/time, jurisdiction, officer ID
Units/parties with drivers, owners, VINs, plates, make/model, and commercial indicators
Location: intersection details, lane designations, mile markers, work zones, detours
Traffic controls: signals, stop/yield signs, temporary control devices
Contributing factors and sequence of events (coded and narrative)
Citations and related statutes
Weather, lighting, road surface, roadway character
Narrative synopsis with contradictions flagged across statements
Diagram interpretation tied to the narrative and photos
Witness list with contact details and prioritization
Third‑party insurer indicators (carrier names, policy numbers, agent references)
Preliminary fault indicators and suggested comparative negligence split per your playbook
All page-level citations so reviewers can verify in seconds

How Doc Chat handles messy real-world documents

Police accident reports are often scanned at low resolution, contain handwriting, or arrive as multi‑generation faxes. Witness statements are free‑form and scattered across the file. Doc Chat uses robust OCR and retrieval techniques to normalize poor quality scans, reconstruct tables, and read handwriting where possible. It also consolidates supplemental pages, continuation narratives, and attachments (e.g., photos, tow slips), and then de‑duplicates repeated pages.

Because the model is trained on your policies and workflows, it knows what “good” looks like for your team. If a critical field is missing — say, no officer narrative or the diagram page is absent — Doc Chat flags the gap and triggers a follow‑up task to request missing pages. This eliminates another common source of delay in subrogation.

Real-time Q&A on massive files

The value isn’t just extraction; it’s interactive analysis. With Doc Chat, an auto claims adjuster can ask:

“Summarize all evidence that Unit 2 failed to yield at a protected left.”
“List all witness statements that corroborate a rear‑end impact sequence.”
“Identify the posted speed limit and each driver’s reported speed.”
“Did the officer assign any contributing factors to the insured?”
“Provide the third‑party insurer and any policy references.”
“Generate a subro notice letter with cited facts and attach relevant pages.”

Answers are immediate, even if the claim file spans hundreds or thousands of pages. Every answer includes a link back to its exact source page, accelerating both desk review and audit. As highlighted by Great American Insurance Group in our claims transformation webinar, this page‑level transparency builds trust with adjusters, managers, auditors, and counsel.

Business impact: faster cycle times, more recoveries, lower LAE

Subrogation is a race against time. The longer it takes to identify liability and responsible parties, the harder recovery becomes. By automating police report processing:

Cycle time shrinks: What once took adjusters 1–3 hours per file to read, highlight, and key into a worksheet now takes minutes. For teams handling hundreds of new files weekly, this translates into days of capacity freed each week and weeks removed from backlogs.

Recovery rates rise: Faster, more consistent identification of third‑party carriers and defensible fault splits increases subrogation notices, improves negotiation leverage, and reduces missed opportunities. Doc Chat also flags patterns — repeat offenders, known loss locations, recurring contractors — that strengthen strategy.

Leakage drops: Page‑level completeness and cross‑checks reduce miss rates on critical facts, decreasing overpayments and leakage. Doc Chat’s consistent application of your playbook also eliminates desk‑to‑desk variance in subrogation referrals.

LAE falls: Less manual document review and rework means fewer overtime hours and lower reliance on external vendors for large file reviews. Adjusters refocus on investigation, negotiation, and customer care.

Employee experience improves: As we note in AI’s Untapped Goldmine: Automating Data Entry, removing repetitive reading and data entry lifts morale and reduces turnover — a major hidden cost in claims operations.

From accident report to subro letter: what the automated output looks like

Doc Chat produces a standardized “Subrogation Fact Pack” designed for rapid action. Typical elements include:

Executive summary: One‑page overview of liability indicators, citations, and proposed comparative negligence.
Structured data sheet: Normalized fields for claim system ingestion (units, VINs, carriers, factors, controls).
Witness outreach list: Prioritized contacts with phone numbers and key statement excerpts.
Third‑party carrier lead: Carrier, policy, and agency details where available, plus suggested outreach script.
Document citations: Links to every supporting page, ready for internal or external counsel review.
Draft subrogation notice: Pre‑populated letter with cited facts and attachments indicated.

This “fact pack” can be exported to your claims platform via API, emailed securely, or downloaded to your document management system.

Evidence consistency checks and fraud signals

Not every police report aligns perfectly with other evidence. Doc Chat compares police narratives and diagrams to repair estimates, scene photos, dashcam footage transcripts, and recorded statements. It flags potential discrepancies — a report indicating a right‑front impact while the estimate shows rear bumper work, or a narrative speed claim inconsistent with skid mark lengths. For SIU, these prompts can trigger deeper review, especially in high‑severity Commercial Auto or GL & Construction matters.

Doc Chat also watches for recurring providers, templated language across unrelated reports, and anomalies in sequence-of-events coding, reflecting the systematic approach described in Reimagining Claims Processing Through AI Transformation. When combined with ISO ClaimSearch reports and loss histories, this creates a more holistic view of risk and opportunity.

How Doc Chat fits into the end-to-end claims workflow

Doc Chat doesn’t force a rip‑and‑replace. Start with drag‑and‑drop uploads and Q&A. Then, integrate via API to your claims system to auto‑populate subrogation referral fields, attach the Subrogation Fact Pack, and trigger workflows for third‑party outreach. Many carriers connect Doc Chat to their intake queues so every new police report is automatically processed within minutes of arrival.

Typical integration points include:

Claims triage: auto‑assign claims with strong recovery indicators to subrogation units
Document management: store structured outputs and citations alongside original reports
Outbound communication: generate subro notices and attach cited evidence packets
Analytics: track recovery rates, cycle times, and desk‑level consistency

Why Nomad Data is the right partner

Doc Chat is built for insurance. We designed it to read the dense, inconsistent documents that govern Auto, Commercial Auto, and GL & Construction claims — and to turn that complexity into consistent, defensible action.

Key advantages for auto claims adjusters and subrogation teams:

Volume: Ingest entire claim files — thousands of pages — and return results in minutes.
Complexity: Extracts and infers from inconsistent forms, narratives, and diagrams, surfacing what matters for liability and recovery.
The Nomad Process: We train Doc Chat on your subrogation playbooks, state‑by‑state rules, and data models, delivering a customized solution that mirrors your best adjusters.
Real‑time Q&A: Ask plain‑language questions across massive files and get instant, cited answers.
Thoroughness: Surfaces every reference to coverage, liability, or damages, reducing leakage and blind spots.
White‑glove implementation: Most teams go live in 1–2 weeks with hands‑on support, sample‑file validation, and role‑based training.

Security and governance are first‑class concerns in claims. Doc Chat supports strict access controls, audit logs, and page‑level explainability, enabling transparent oversight for compliance, reinsurers, and regulators — the same controls that built trust with GAIG’s claims organization.

Handling state-specific crash forms and codes

Doc Chat includes libraries for common state crash forms, with pipelines tuned to the nuances of each. Examples include:

Texas CR‑3 (TxDOT): unit/person codes, contributing factors, diagram annotations
California CHP 555: party and witness boxes, sketch interpretations, special conditions
New York NYPD MV‑104A and MV‑104: multi‑page narratives, witness statements
Florida HSMV 90010S: citation and signal control sections, long/short form variations
Illinois Traffic Crash Report: sequence‑of‑events coding and roadway geometry

These pipelines normalize disparate codes (e.g., contributing factors) into a carrier‑level taxonomy. That means better dashboards, cleaner analytics, and more consistent decisioning across desks and states.

Beyond the police report: related documents Doc Chat processes for subro

To support a complete subrogation package, Doc Chat also reads:

FNOL forms and adjuster notes for initial facts and coverage
Witness statements (written, transcribed, or recorded audio transcripts)
Repair estimates and photos to validate impact mechanics
Demand letters and counsel correspondence
ISO ClaimSearch reports and loss histories
Tow and storage invoices, impound records
Commercial documents: bills of lading, delivery schedules, jobsite permits
Policy declaration pages and endorsements (e.g., MCS‑90 for motor carriers) when relevant

When these are present, Doc Chat cross‑references them with the police report to catch discrepancies and strengthen your negotiation stance.

Quantifying the upside: a conservative scenario

Consider an Auto and Commercial Auto claims team processing 2,000 police reports per month. If manual review averages 1.5 hours per report (reading, highlighting, data entry), that’s 3,000 hours monthly. Doc Chat reduces this to minutes per file, conservatively saving 2,000+ hours per month. At $50/hour fully loaded, that’s $100,000+ in monthly LAE savings before counting increased recoveries.

Now consider recovery uplift. If faster, more consistent decisions surface just 5% more viable subrogation opportunities — say, 100 additional notices monthly with an average net recovery of $1,200 — that’s $120,000 in additional monthly recoveries. Combined with LAE savings, the financial case compounds quickly.

From skepticism to trust: change management done right

Adjusters gain trust by testing Doc Chat on cases they know cold. We encourage teams to load a few challenging files, ask hard questions (“Which facts support a 70/30 split under Florida law?”), and verify the citations. As described in our GAIG story, consistent accuracy plus page‑level explainability converts skeptics into champions.

Just as important, Doc Chat keeps humans in the loop. Think of it as a highly capable junior teammate who never tires. It reads everything; you apply judgment. This aligns with the best‑practice model we outline in Reimagining Claims Processing Through AI Transformation: AI accelerates and standardizes; humans decide.

Implementation in 1–2 weeks: what to expect

Nomad’s white‑glove onboarding is simple and fast:

Discovery (days 1–2): Review your subrogation playbooks, forms mix, and claim system fields.
Tuning (days 3–7): Train Doc Chat on your rules and sample files; finalize extraction schemas; define your Subrogation Fact Pack format.
Pilot (days 8–10): Adjusters test with live files via drag‑and‑drop; we iterate on prompts and outputs.
Go‑live (days 11–14): Enable API integration to your claim system and DMS; deliver role‑specific training; stand up dashboards.

Throughout, your team gets hands‑on support. No data science resources required. Most carriers start seeing value in week one — and hard ROI in the first 30 days.

Security, auditability, and defensibility

Subrogation outcomes must stand up to scrutiny. Doc Chat provides:

Page‑level citations for every extracted fact and every Q&A answer
Complete audit trails with time‑stamped logs of actions and outputs
Role‑based access controls and secure document handling

The combination of speed and defensibility is what makes Doc Chat the best answer when teams search for the best tool to automate accident report analysis. You get immediate productivity plus the evidence chain your QA, legal, and compliance stakeholders require.

Where the gains are greatest

While all claim teams benefit, early adopters often see outsized impact in:

High‑volume Auto: Fast triage, consistent subro referrals, reduced backlog.
Mixed Auto/Commercial Auto: Quick identification of commercial carriers and policy clues; better coordination with SIU when needed.
GL & Construction: Rapid detection of work zone elements and contract‑driven liability shifts.

In every scenario, Doc Chat aligns resources to cases with true recovery potential — not just the cases that are easiest to process manually.

Common questions from Auto Claims Adjusters

Can Doc Chat handle handwritten officer notes? Yes. It applies handwriting recognition where possible and always cites back to the source page. If a note is illegible, it flags the ambiguity rather than filling gaps with guesswork.

What about non‑standard or legacy forms? Doc Chat recognizes hundreds of patterns and adapts rapidly. During onboarding, we include examples of your less common forms to optimize performance.

Does it understand comparative negligence across states? Doc Chat doesn’t make legal determinations; it applies your guidelines and highlights facts that typically drive fault splits in your jurisdictions, with citations that make the reasoning clear.

Will this replace adjusters? No. It removes the reading and rote extraction so adjusters can focus on investigation, negotiation, and service. As we argue in The End of Medical File Review Bottlenecks, the payoff is better work and better outcomes — not fewer professionals.

How searchers find success with Doc Chat

Insurance teams increasingly search for practical, proven AI. If your team is looking for “AI to extract info from police reports for subrogation,” “Best tool to automate accident report analysis,” or ways to “Automate subrogation with police report processing,” Doc Chat is the fastest path from evaluation to ROI. It reads what humans read, presents what humans need, and respects how claims decisions get made.

Getting started

We recommend a short, focused pilot:

Pick 50–100 recent claims with police reports across a few high‑volume states.
Define your Subrogation Fact Pack format and the fields you want populated in your claim system.
Have adjusters and subrogation specialists use Doc Chat for two weeks on real files, asking live questions and verifying citations.
Compare cycle times, referral rates, and recovery indicators to a matched control group.
Decide on phased integration (starting with drag‑and‑drop) or direct API ingestion to your claims platform.

Within days, you’ll see the difference in speed, accuracy, and confidence. Within weeks, you’ll feel it in your backlog and your recovery numbers.

Conclusion: make subrogation speed your advantage

Police accident reports are the backbone of auto liability and subrogation, but they have historically been a drag on speed and consistency. Doc Chat changes that. It reads every page, extracts every fact that matters, and gives auto claims adjusters instant, defensible answers — all while fitting the way your team already works.

If your goal is to automate subrogation with police report processing, there’s no faster path to value than trying Doc Chat on your next batch of reports. Learn more and see it in action at Doc Chat for Insurance.