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

Speeding Up Subrogation: Automated Extraction from Police Accident Reports — Built for Subrogation Specialists

Every day, subrogation specialists in Auto, Commercial Auto, and General Liability & Construction wade through police accident reports, state crash forms, and witness statements to piece together the story of liability. The challenge is not just volume; it’s variability. Texas CR‑3 reports, California CHP 555 forms, New York MV‑104A, and dozens of other formats bury key facts in check boxes, codes, handwritten narratives, and diagrams. Delays in finding a responsible party can mean missed notice deadlines, lost evidence, and ultimately lower recoveries. Nomad Data’s Doc Chat fixes this bottleneck by turning cumbersome packets into instant answers—automatically extracting every liable party, policy number, citation, and contributory factor straight from the source documents.

Doc Chat is a suite of purpose‑built, AI‑powered agents designed for insurance. For subrogation specialists, it ingests entire claim files, reads police accident reports, state crash forms, and witness statements at scale, and returns structured facts, liability indicators, and recovery opportunities in minutes. Ask a plain‑language question like, “Who was cited and for what statute?” or “List all witnesses with contact info and where they were standing,” and get precise answers with page‑level citations. If you’ve been searching for AI to extract info from police reports for subrogation or the best tool to automate accident report analysis, this is it.

The subrogation reality in Auto, Commercial Auto, and General Liability & Construction

Subrogation specialists work under the pressure of the clock. Whether it’s a rear‑end crash involving a personal auto, a lane‑change collision with a box truck, or a jobsite incident where a contractor’s vehicle damaged third‑party property, the path to recovery starts with documents—police accident reports, state crash forms, officer narratives, supplemental reports, photographs, and witness statements.

In Auto and Commercial Auto, you’re often the first to translate complex reports into a viable recovery strategy. In General Liability & Construction, you may be pulling facts from both police documentation and jobsite materials—site incident reports, OSHA 301 logs, superintendent daily reports, and subcontractor agreements—to determine whether indemnity, additional insured endorsements, or roadway work zone signage played a role. Different lines of business, same pain: evidence is scattered across inconsistent formats and buried in codes that differ by jurisdiction.

Why police accident reports are so hard to process at scale

On paper, crash forms are standardized. In practice, every state uses different templates, codebooks, and checklists. A subrogation specialist might confront any of the following on a given day:

Texas CR‑3 (TxDOT), California CHP 555, Florida Long Form, New York MV‑104A, Illinois SR 1050, Pennsylvania AA‑500, and many others—each with unique fields and coding schemes.
MMUCC-coded fields for crash manner, harmful event sequence, driver contributing factors, injury severity (KABCO scale), and lighting/road conditions.
Officer narratives and diagrams that require interpretation to determine right‑of‑way, signal phases, lane position, or left‑turn presumptions.
Handwritten witness statements, often faxed or scanned, with variable legibility.
Supplemental updates with corrected VINs, revised citations, or added witnesses days or weeks later.

The information you actually need for subrogation—liable party identifiers, citations and statutes, adverse carrier and policy numbers, vehicle ownership, towing and storage locations, coverage triggers, and witness contact info—rarely appears in one place. It’s inferred across narratives, checkboxes, and attachments. That’s why searching for a way to automate subrogation with police report processing has become a top priority for many teams.

How the manual process slows recoveries and inflates costs

Even the most experienced subrogation specialists follow a time‑intensive manual path today. The steps look familiar because they are the job:

Open the police accident report, skim the front page for crash date, time, jurisdiction, and crash number (e.g., CRIS ID, case number, ORI), then bounce to the officer narrative.
Decode contributing factor codes and light/road conditions using state‑specific MMUCC guides.
Interpret the crash diagram to identify impact points, movement prior to crash, and signal control, then reconcile with narratives.
Extract driver, owner, and insurer data for each unit: names, addresses, phone numbers, VIN, plate, carrier, and policy number; then verify in core systems.
Scan for citations, statutes, BAC/tox screens, DRE notes, and any enforcement action.
Aggregate witness statements, capture contact details, and map sight lines or positions if provided.
Reconcile contradictions between the narrative, diagram, and witness accounts; annotate with questions or follow‑ups for adjusters or SIU.
Draft subrogation notice letters, spoliation/litigation hold letters (for dashcam/ECM/telematics preservation), and requests to tow yards or municipalities.
Calendar notice-of-claim deadlines (e.g., municipal 30–90 day requirements), statute of limitations, and arbitration filing windows.
Package evidence for intercompany negotiation or arbitration, then monitor for responses and missing documentation.

This manual pattern introduces bottlenecks and risk. If your team is slammed and a police report sits for ten days, a witness who was cooperative on day one may be unreachable by day ten; a tow yard may sell a vehicle; a municipal notice window may close. In Commercial Auto, one missed spoliation letter can erase telematics data that would have secured a six‑figure recovery. In Construction GL, failing to spot the right additional insured endorsement (e.g., CG 20 10/CG 20 37) tied to a subcontractor with a vehicle involved can forfeit a tender opportunity. Multiply these by hundreds of files and the leakage adds up.

Insert Doc Chat: purpose‑built AI to extract the facts that drive recovery

Doc Chat by Nomad Data changes the equation. It ingests the entire claim file—police accident reports, state crash forms, citizen reports, supplemental officer updates, witness statements, photos, demand letters, ISO claim reports, FNOL forms, repair estimates, scene diagrams—and returns structured, verified facts with page‑level citations. It’s not just OCR. It’s comprehension plus inference, trained on your subrogation playbook.

What makes Doc Chat different is how it handles variability and complexity. The system reads both the codes and the story. It cross‑checks the crash diagram against the narrative to detect inconsistencies, links each vehicle’s owner to insurers and policy numbers, and flags missing data that could hinder recovery. It also standardizes outputs across jurisdictions—so your Texas CR‑3 data aligns cleanly with your California CHP 555 extraction—giving subrogation specialists a single, reliable view of liability factors and recovery targets.

AI to extract info from police reports for subrogation: what exactly gets pulled?

In a matter of seconds, Doc Chat can surface the fields subrogation specialists need most to make a determination and take action. Examples include:

Parties and roles: drivers, owners, commercial carriers, employers, pedestrians/cyclists, passengers, and roadway authorities if alleged defects are present.
Vehicle and coverage: VIN, plate/state, vehicle type (e.g., tractor, trailer, box truck, pickup), registered owner, adverse carrier, and policy number.
Liability signals: cited driver, statute code, narrative admissions, primary/contributing factors, right‑of‑way findings, traffic control devices, speed limits, and sight obstruction references.
Sequence of events: unit movement, harmful events, impact points, lane positions, and pre‑impact actions (backing, left turn, lane change, following too closely).
Injury/property damage: severity codes (KABCO), property owner identification, business impact, and municipal property involvement.
Witness data: names, phone/email, statements, vantage points, and contradictions between witness versions and officer narratives.
Evidence preservation: tow yard name/address, storage dates, dashcam/ECM/telematics presence, and suggested spoliation language.
Deadlines and venues: municipal notice requirements hinted by agency ownership, probable arbitration forum eligibility, and statute reminders.

The output is delivered as a clean, structured summary with links back to each originating page. If you ask, “Show me every mention of the red Toyota’s turn signal status,” Doc Chat returns the answer and every place in the file where that signal is referenced. This is why carriers describe Doc Chat as the best tool to automate accident report analysis. It doesn’t just summarize; it builds a case‑ready set of facts you can trust.

From manual review to automation: a side‑by‑side

Today’s manual subrogation review takes hours per claim and weeks for a backlog. With Doc Chat, the workflow flips:

Ingest: Drag and drop a PDF bundle of police accident reports, state crash forms, and witness statements—or set up an inbox/watch folder. Doc Chat processes hundreds of files in parallel.
Extract and normalize: The AI reads codebooks, recognizes handwriting, and converts diverse crash forms into normalized fields. It maps variations like “Following Too Closely,” “Tailgating,” and “Unsafe Speed” to your internal taxonomy.
Analyze liability: The agent correlates citations, diagram vectors, statements, and roadway controls to estimate likely liability shares (subject to your playbook) and flags red‑flag contradictions for human review.
Act: It drafts subrogation notices, generates spoliation/lit hold letters, pre‑populates arbitration exhibits, and creates task lists—ready for your approval. It can also push structured data into claim systems via API.
Ask anything: Need a witness‑only timeline? Want the officer’s narrative distilled into numbered facts? Ask and get page‑linked answers instantly.

What once took a subrogation specialist two to four hours often collapses into minutes. For heavy Commercial Auto programs or GL files that include both police and construction documentation, the delta is even greater.

Automate subrogation with police report processing: impact by line of business

While the documents and exposures vary, the payoff is consistent across Auto, Commercial Auto, and General Liability & Construction.

Auto: Quickly identify adverse carrier, policy number, and citations to send subrogation notices within days of FNOL. Surface witness contradictions that support liability. Extract PIP/MedPay details and coordinate recoveries and reimbursements.

Commercial Auto: Tie drivers to employers, employers to fleets, and fleets to carriers. Preserve dashcam and telematics data immediately with auto‑generated spoliation letters. Detect complex liability scenarios (e.g., left‑turn across path plus speed differential) from multi‑vehicle chain reactions.

General Liability & Construction: Blend police accident facts with site incident reports, OSHA 301 entries, daily logs, and subcontractor agreements to determine whether contractual indemnity and additional insured endorsements trigger tenders. Identify municipal or contractor roadway signage/traffic control issues when a construction zone is implicated.

Business impact: faster recoveries, lower LAE, fewer misses

Doc Chat is engineered to deliver measurable outcomes for subrogation teams:

Cycle time reduction: Move from days of reading to minutes of action. Many carriers see 5–10x faster subro identification on new crashes.
Higher recovery rate: Identify liable parties and coverage more consistently, even in messy files. Fewer missed notice windows and better evidence preservation translate into more dollars recovered.
Lower loss‑adjustment expense (LAE): Reduce manual extraction and document prep. Shift expert time from reading to negotiating and arbitrating.
Consistency and defensibility: Page‑level citations and standardized outputs reduce variance across specialists and desks, supporting audit, reinsurance, and regulatory review.
Happier teams and lower turnover: Specialists spend less time decoding codes and more time applying judgment and strategy.

In complex claims, AI accuracy holds steady while human accuracy drops with page count. That’s why customers report dramatic time and accuracy gains when they deploy Doc Chat for high‑volume police report processing. Great American Insurance Group’s experience—cutting days of review to minutes while maintaining page‑level traceability—illustrates the scale of change possible with modern AI in claims and subrogation. Read more: Reimagining Insurance Claims Management.

How Doc Chat handles the edge cases that derail traditional tools

Subrogation work is full of exceptions. Doc Chat is built for them:

Handwriting and faxes: Advanced OCR plus language models reconstruct officer notes, witness statements, and diagrams—even when scanned multiple times. The system shows you the source every time for quick validation.

Codebooks and synonyms: The agent maps state‑specific MMUCC codes and normalizes equivalent terms (e.g., “fail to yield,” “right‑of‑way,” “yield sign violation”). It stores your team’s preferred taxonomy, so outputs match your reports and dashboards.

Conflicting narratives: When a diagram shows one story and a narrative suggests another, Doc Chat flags the discrepancy and lists specific follow‑ups—request traffic signal timing, check intersection cameras, contact listed towing yard for dashcam retrieval, or re‑interview a witness whose vantage point contradicts the officer’s assessment.

Contractual layers (GL & Construction): If the crash occurred in or near a work zone, the system highlights references to traffic control set‑ups, contractor names, and permits, and suggests reviewing site documents and agreements for indemnity and additional insured clauses.

Generate letters, forms, and arbitration‑ready packets with one click

Once the facts are extracted, Doc Chat drafts the artifacts subrogation specialists generate daily:

Subrogation notices to adverse carriers with claim numbers, crash details, and legal bases.
Spoliation/litigation hold letters targeted to employers, fleets, tow yards, and municipalities to preserve dashcam, ECM, or roadway maintenance records.
Requests for reports and exhibits from law enforcement or DOT repositories (e.g., CRIS, CHP).
Arbitration packets with exhibits labeled and page‑cited (police reports, photos, witness statements, estimates), ready to upload to your forum of choice.
Internal recovery memos summarizing liability, exposure, and next actions for manager approval.

Because outputs are tied to your playbook, the language, tone, and thresholds (e.g., when to tender vs. negotiate) reflect your standards. For a deeper discussion on why this kind of intelligent document automation isn’t just “web scraping for PDFs,” see Beyond Extraction.

Security, auditability, and explainability built for insurance

Claims files carry sensitive PII and PHI. Nomad Data maintains enterprise‑grade security and clear governance. Every answer in Doc Chat comes with page‑level provenance so your team—and auditors—can verify. Outputs are consistent and defensible across teams, which is crucial for recoveries that go to arbitration or litigation. Doc Chat’s real‑time Q&A feature enables instant spot checks when supervisors or counsel need to confirm a detail on the fly. Learn how carriers secure trust and control with page‑linked outputs in our field story with GAIG above.

Why Nomad Data is the best solution for subrogation teams

Nomad Data’s Doc Chat is purpose‑built for claims and subrogation and it shows in the results:

Volume: Ingests entire claim files—thousands of pages—in minutes without adding headcount.

Complexity: Excels at dense, inconsistent crash forms, diagrams, and narratives that defeat template‑based tools.

The Nomad Process: We train Doc Chat on your subrogation playbooks, document types, and standards—creating a custom agent that speaks your language.

Real‑Time Q&A: Ask “List all medications administered on scene,” “Did any witness mention the traffic light phase?” or “Extract every mention of ‘unsafe speed’ and show the pages.” Immediate answers, complete with citations.

Thorough & Complete: Surfaces every reference to coverage, liability, and damages to eliminate blind spots and leakage.

Your Partner in AI: Nomad is not just software. It’s white‑glove implementation, ongoing optimization, and a collaborative roadmap tied to your recovery KPIs.

Implementation is measured in days, not quarters. Most subrogation teams go from kickoff to live use in 1–2 weeks, with no heavy IT lift. Start with drag‑and‑drop uploads; integrate via API when you’re ready. For more on rapid rollouts and measurable improvements, read Reimagining Claims Processing Through AI Transformation and explore Doc Chat for insurance on our product page: Doc Chat for Insurance.

From data entry to decision support: where the ROI comes from

A large share of subrogation labor is sophisticated data entry—finding the right facts, structuring them, and deciding what to do. By automating extraction and first‑pass analysis, Doc Chat unlocks dramatic ROI. Specialists spend their time on negotiation, tendering, and arbitration strategy rather than decoding forms. For a broader look at why automating document data entry generates outsized returns, see AI’s Untapped Goldmine: Automating Data Entry.

Practical example: turning a Texas CR‑3 into a recovery in 48 hours

Consider a Commercial Auto rear‑end crash on I‑35 involving a fleet pickup and a passenger sedan. You receive a Texas CR‑3, two supplemental officer reports, a handwritten witness statement, tow receipt, and six photos.

Manual path: Two hours extracting parties, VINs, coverage, and citations; another hour reconciling narrative with diagram; 30 minutes drafting notice and spoliation letters; additional time to calendar deadlines, verify adverse carrier, and request dashcam preservation.

Doc Chat path: Upload the packet. In under five minutes, get a structured summary: liable party as indicated by citation for Transportation Code 545.062 (following distance), adverse carrier name/policy, witness contact details, tow location, and officer ID. Doc Chat flags the vehicle’s telematics timestamp in a supplemental note and auto‑drafts letters to the fleet manager and tow yard to preserve dashcam/ECM data. You approve and send the same day. Recovery negotiations begin within 48 hours, not two weeks.

Extending to GL & Construction: police reports plus contracts and site logs

When crashes occur in or near road construction zones, subrogation can hinge on contractual risk transfer and compliance with traffic control plans. Doc Chat reads both the police accident report and your construction file—site incident reports, daily logs, OSHA 301/300 entries, subcontractor agreements, COIs, and endorsements like CG 20 10 and CG 20 37. It:

Identifies contractors named in the police narrative and cross‑references them with your project’s subcontractor roster.
Surfaces applicable additional insured status and indemnity clauses that enable tender or cross‑subrogation.
Flags references to missing or improper signage, lane closures, or flagging noted by officers or witnesses.

This blended view allows GL subrogation specialists to make quicker, more defensible determinations and act while evidence is still fresh.

Addressing adoption concerns: accuracy, security, and control

Two questions come up in every subrogation conversation: “Will the AI hallucinate?” and “Is our data safe?” In extraction use cases like police reports, well‑tuned models perform exceptionally because they’re constrained to what’s on the page and to your team’s instructions. Every answer carries a citation for verification. On security, Nomad adheres to industry best practices and treats customer data with enterprise safeguards and governance. For a candid look at how we build trust into high‑stakes document workflows, explore The End of Medical File Review Bottlenecks.

Searchers’ corner: findability for subrogation leaders

If you’re actively evaluating options, you’re likely searching phrases such as:

AI to extract info from police reports for subrogation
Best tool to automate accident report analysis
Automate subrogation with police report processing

Doc Chat directly addresses these needs by transforming police accident reports, state crash forms, and witness statements into structured, actionable insights—fast enough to change outcomes, not just produce summaries.

Implementation: white‑glove in 1–2 weeks

Unlike generic tools that take months to configure and still miss the nuances of subrogation, Nomad delivers a white‑glove onboarding tailored to your line‑of‑business mix and state footprint. In 1–2 weeks, we:

Collect representative police accident reports and state crash forms across your top jurisdictions.
Encode your subrogation playbook: liability standards, notice templates, thresholds for arbitration vs. negotiation, and required exhibits.
Stand up drag‑and‑drop ingestion and define outputs—spreadsheets, PDFs, or direct API feeds to your claims/subrogation platform.
Train your specialists on real‑time Q&A and exceptions handling.
Validate accuracy with side‑by‑side comparisons on historical cases, then go live.

You can start small with a team inbox or pilot line of business, then scale to all Auto, Commercial Auto, and GL subrogation desks. Because the agent learns your formats and preferences, the experience gets better every week.

What success looks like in a quarter

Subrogation leaders typically target three outcomes in the first 90 days:

1) Faster time‑to‑notice: Reduce the average time from police report receipt to subrogation notice by 60–80%—capturing more evidence and preserving more data.

2) Higher recovery yield: Increase the percentage of files with identified liable parties and adverse coverage by 10–20 points, driven by fewer misses in complex, multi‑unit crashes.

3) Lower LAE per recovered dollar: Cut hours of manual extraction and packet prep; redeploy specialist time to negotiation and arbitration where it moves the numbers.

From there, teams expand Doc Chat into related workflows: demand package analysis, legal letter triage, ISO claim report review, repair estimate validation, and fraud pattern checking for repeat providers or staged accident indicators.

Why now: the competitive gap is widening

AI‑assisted subrogation isn’t a future state—it’s the current bar for speed and thoroughness. As more carriers and TPAs leverage Doc Chat, they shorten subro cycles, preserve more evidence, and standardize quality across teams. Those advantages compound. If your organization still relies on purely manual processing of police accident reports and state crash forms, your recoveries are competing against teams armed with instant extraction, cross‑checks, and page‑linked facts.

Take the next step

If your team is searching for the best tool to automate accident report analysis or a reliable way to automate subrogation with police report processing, schedule a working session. Bring three to five real files—police accident reports, state crash forms, and witness statements—and watch Doc Chat extract, normalize, and recommend next actions in minutes. See why leading carriers trust Nomad as their partner for high‑stakes document intelligence.

Learn more and get started here: Nomad Data Doc Chat for Insurance.