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

Speeding Up Subrogation: Automated Extraction from Police Accident Reports for Auto Claims Adjusters

Every Auto Claims Adjuster knows the feeling: a new claim drops with a thick police accident report, state crash forms, and a handful of witness statements. The clock starts ticking to identify liability, preserve subrogation rights, and move the file toward resolution. The challenge is not lack of information but the time and precision required to mine it. This is precisely where Doc Chat by Nomad Data changes the game. Doc Chat is a suite of purpose-built, AI-powered agents that extract key facts, summarize documents, and answer complex questions across entire claim files, so adjusters can determine recoverability and subrogation potential in minutes rather than days.

In Auto, Commercial Auto, and even General Liability & Construction incidents involving vehicles, subrogation viability often hinges on what is written — or implied — in a police crash narrative, diagram, or citation section. Doc Chat ingests police accident reports, state crash forms, and witness statements, then cross-references the details against policy terms, jurisdictional fault standards, and internal subrogation playbooks. Instead of scrolling line by line, adjusters ask plain-language questions and receive instant answers with page-level citations. If you are searching for the best tool to automate accident report analysis or exploring AI to extract info from police reports for subrogation, this article shows how Doc Chat delivers measurable impact.

The subrogation problem in Auto, Commercial Auto, and GL & Construction

For Auto Claims Adjusters handling passenger vehicles, light trucks, or commercial fleets, subrogation opportunities can be numerous yet fleeting. Police reports are critical, but they are inconsistent across jurisdictions. One state’s crash report places contributing circumstances on the first page; another embeds them in a supplemental narrative. Some officers complete a diagram with lane markers and impact points; others rely on checkboxes and coded fields. In General Liability & Construction scenarios that involve vehicles on job sites, adjusters must piece together how contractors, subs, and equipment contributed to loss. The nuance of who owned or controlled the vehicle, who had the right-of-way, whether a citation was issued, and whether a construction site traffic control plan existed can materially change recovery prospects.

In Auto and Commercial Auto, subrogation success often depends on quickly confirming: who was cited, which statute applies, whether a signal was violated, whether a professional driver was on the clock, whether comparative negligence applies in the jurisdiction, and whether an employer may share liability. In GL & Construction, adjusters face added layers — hold-harmless agreements, additional insured endorsements, site safety logs, and responsibility matrices among general contractors and subs. When a skid steer or delivery truck strikes a pedestrian inside a controlled construction zone, the police crash form alone rarely tells the whole story. Yet it still holds foundational facts that must be extracted fast to set the recovery strategy, issue timely notices, and avoid spoliation or notice pitfalls.

How the process is handled manually today

Manual review remains the default. An Auto Claims Adjuster opens a PDF police accident report, scans for speaking narratives, pulls unit numbers, matches them to drivers and owners, reads the diagram, checks the manner of collision, and hunts for contributing factors and citations. If the incident involves a commercial vehicle, they look for DOT numbers, employer names, or indications of a for-hire carrier. With state crash forms, they reference code sheets to decode checkboxes like contributing circumstance, sequence of events, or roadway conditions. They then repeat this across supplemental reports and witness statements, often re-keying data into the claims system.

Typical manual steps include:

Opening the police accident report, state crash forms, and any addenda; locating the narrative and diagram.
Identifying each unit and occupant; mapping unit numbers to drivers, owners, and employers.
Documenting citations, statutes, and officer opinions (if present); deciphering checkboxes and code tables.
Reading witness statements for corroboration or inconsistencies; extracting time, speed, and signal phases.
Cross-checking with FNOL notes, photos, dashcam snippets, towing invoices, and repair estimates.
Comparing facts to coverage terms and subrogation playbooks; applying jurisdictional negligence rules.
Entering structured fields into the claim system and drafting a preliminary subrogation memo or demand.

This work is thoughtful but slow. Volume spikes, severe injury claims, or big-loss Commercial Auto collisions can leave adjusters stretched thin. Errors appear when fatigue sets in: a missed citation, a misread unit number, a skipped checkbox that flips the liability story. When subrogation notices are delayed, opportunities shrink.

Why manual extraction fails at scale

Police accident reports vary widely: Texas CR-3 forms differ from Florida long-form crash reports; California CHP 555 is not the same as New York MV-104A. Officers sometimes attach hand-drawn diagrams or multiple supplements. Witness statements may be typed, handwritten, or captured as scanned images. In GL & Construction crashes, the report may identify a jobsite but omit who controlled ingress-egress or traffic flagging. What makes this difficult is the inference required: the crucial recovery information is often not in a neat field but implied across narrative text, diagram symbology, and coded checkboxes. As Nomad Data explains in Beyond Extraction — Why Document Scraping Isn’t Just Web Scraping for PDFs, document intelligence is about building the inferences a skilled adjuster would make, not just lifting data points.

As claim volumes rise and police report formats proliferate, manual extraction becomes a bottleneck. Files sit untouched; reserve decisions wait for facts; notice letters go out late; and subrogation leakage grows. Meanwhile, policyholders and risk managers demand faster, clearer answers.

How Doc Chat automates subrogation-focused accident report processing

Doc Chat for Insurance ingests entire claim files — from police accident reports and state crash forms to witness statements and correspondence — and returns structured, defensible outputs in minutes. Adjusters ask targeted questions, such as: Who received citations and under what statutes? What did the diagram show about point of impact? Did any witness corroborate a red-light allegation? Was the driver on duty for a commercial employer? Doc Chat answers instantly and provides source-page citations for easy validation.

For Auto, Commercial Auto, and GL & Construction incidents, Doc Chat can be configured to extract subrogation-critical facts, including:

Unit-to-driver mapping, owners, VINs, plates, DOT numbers, and carrier/employer names
Citations issued, statutes cited, and officer opinion of fault (where applicable)
Signal control and status, lane assignments, roadway conditions, weather, visibility, and speed estimates
Manner of collision, sequence of events, pre-impact actions, point-of-impact markers, and diagram annotations
Witness identities, observations, and consistency versus driver narratives
Location specifics: intersection names, mile markers, work-zone indicators, detours, or temporary traffic control
Injury severity indicators, ambulance transport, and hospital destinations for downstream demand management
Indications of employer vehicle use, livery/for-hire, or contractor operations relevant to GL & Construction risk transfer

Once these elements are extracted, Doc Chat applies your jurisdictional negligence standards and internal subrogation playbooks to frame potential recovery pathways. It flags comparative fault issues, time-sensitive notice requirements, and likely adverse carrier contacts. It also drafts a preliminary demand framework and a follow-up question list for recorded statements or scene investigations. If you are focused on AI to extract info from police reports for subrogation or looking to automate subrogation with police report processing, this is where automation directly translates to earlier, stronger recoveries.

Inside the workflow: from intake to demand

Doc Chat mirrors the way top-performing Auto Claims Adjusters work, only at machine speed. During intake, the system detects document types — police accident report, state crash form, witness statement, officer supplement — and organizes them into a standard review sequence. It produces a subrogation fact sheet summarizing parties, vehicles, citations, and diagram pointers. Adjusters can then ask real-time questions like: List all references to stop-sign control. Show me every mention of speed. Which witnesses reference lane changes? Provide a timeline of events from first sighting to impact. Doc Chat links each answer back to the exact page and line so oversight, SIU, or counsel can verify with one click.

From there, Doc Chat drafts a preliminary subrogation memo tailored to your format: facts, liability theory, applicable jurisdictional standard, and next actions. It can pre-fill notice letters to adverse carriers, municipalities, contractors, or employers. For Commercial Auto and GL & Construction matters, the system also highlights potential indemnity and additional insured angles by cross-referencing policy files, contracts, and certificates where available.

What makes Doc Chat different for Auto Claims Adjusters

Not all document AI is created equal. As shared in Reimagining Insurance Claims Management: Great American Insurance Group Accelerates Complex Claims with AI, Nomad’s approach empowers adjusters to ask questions and get answers with instant citations, building trust and speeding decisions. For Auto Claims Adjusters who must quickly determine fault and recoverability, Doc Chat delivers:

Depth at volume — It ingests thousands of pages per claim and processes roughly 250,000 pages per minute, maintaining consistent accuracy from page 1 to page 1,500.

Subrogation-ready outputs — It formats facts into your subrogation worksheets, recovery memos, and notice templates, so next actions are automatic.

Real-time Q&A — It enables natural-language questions across entire files, with source-cited answers that stand up to audit, reinsurer, or legal review.

Customization via your playbooks — It is trained on your internal standards, preferred phraseology, and jurisdictional nuances, reflecting the way your Auto Claims Adjusters and subrogation partners operate.

Where the time goes today — and how much you get back

Reviewing a single police accident report and its supplements can take hours, especially when cross-referencing witness statements and state code tables. Drafting a subrogation memo and initial notices takes more time. When multiplied across an adjuster’s inventory, days disappear. Nomad clients routinely report that what used to take 5–10 hours of reading and typing is now completed in minutes, with better consistency and fewer misses. In complex bodily injury claims with multi-vehicle crashes, manual reviews that spanned weeks are now reduced to under an hour. As highlighted in The End of Medical File Review Bottlenecks, standardization and speed are the new normal when document intelligence is done right.

Field examples: police accident reports and state crash forms

Doc Chat has seen a wide range of report formats and structures, including state-specific forms such as Texas CR-3, California CHP 555, Florida long form/short form crash reports, New York MV-104A, Georgia’s crash report, and Illinois SR 1050. It normalizes the variability by mapping unit numbers, resolving parties to drivers and owners, and decoding contributing circumstances. The system extracts statute references tied to citations and pulls diagram annotations like impact points and vehicle paths.

From a subrogation standpoint, the critical lift is not just data capture but inference. For example, Doc Chat might connect a witness statement noting the other driver ‘blew the red’ at 7:42 PM with the officer’s diagram showing Vehicle 2 entering from the southbound left-turn lane and an issued citation under a specific traffic code. It surfaces the combination, flags a stronger recovery position, and drops the facts into your memo and demand template. That is the difference between extraction and actionable intelligence.

Beyond Auto: Commercial Auto and GL & Construction nuances

Commercial Auto adds employer and vicarious liability considerations: was the driver on duty, is there a motor carrier record, was this a for-hire livery, and does the employer’s policy attach? Doc Chat pulls employer names, DOT numbers, and operating authorities when present in police reports or attached cargo documents, and it aligns these with policy files. For GL & Construction incidents involving vehicles on job sites, Doc Chat notes work-zone indicators, traffic control references, and mentions of flaggers or contractors. It also flags that separate contracts, hold-harmless agreements, or additional insured endorsements may drive recovery strategy beyond the crash form itself. When those documents are in the file, Doc Chat reads them too, connecting risk-transfer dots in the same workflow.

Accuracy and defensibility: page-level citations and audit trails

Adjusters, managers, and litigators require traceability. Doc Chat delivers page-level citations for every answer. Oversight teams can click from a subrogation memo directly to the police narrative sentence or diagram legend that supports a conclusion. This transparency instills confidence with regulators, reinsurers, and courts. As GAIG’s experience showed in Nomad’s webinar recap, page-linked answers cut review time and build trust across the organization.

AI to extract info from police reports for subrogation: what exactly is captured

When teams search for AI to extract info from police reports for subrogation, they typically want highly specific, repeatable fields and inferences. Doc Chat’s subrogation preset can be configured to produce a consistent, spreadsheet-ready output such as:

Claim identifiers, loss date, loss location, jurisdiction
Vehicle and unit mapping, driver, owner, employer/carrier, VINs, plates
Citations and statutes; officer opinion of fault (if provided)
Traffic control type and status; lane and turn movements
Sequence of events, first harmful event, manner of collision
Diagram reference points and impact markers
Witness roster, contact details, summary of observations, alignment with or deviation from driver accounts
Environmental factors: weather, lighting, roadway surface
Commercial flags: DOT number, for-hire indicator, bill of lading references
GL & Construction markers: work zone, flaggers, GC/sub names if noted
Subrogation viability indicator, comparative fault notes, notice deadlines
Recommended adverse contact, suggested evidence requests, and next investigative steps

With one click, the output can feed your claim system or subrogation management platform, enabling immediate demand drafting and task creation.

Best tool to automate accident report analysis: why Doc Chat stands out

If you are evaluating the best tool to automate accident report analysis, prioritize a solution that reads like an experienced adjuster, not a generic summarizer. Doc Chat was designed for insurance. As outlined in AI’s Untapped Goldmine: Automating Data Entry, Nomad’s infrastructure supports enterprise-scale throughput with robust failure handling and workflow integration, while the solution is tailored to your playbooks rather than forcing you into a one-size-fits-all model. The result is fast adoption, high trust, and immediate ROI.

Automate subrogation with police report processing — end-to-end

Doc Chat does more than highlight a citation or list unit numbers. It moves the subrogation workflow forward. After extracting facts and producing a liability-oriented summary, it drafts notices and demand outlines, proposes evidence follow-ups, and sets reminders for deadlines that vary by jurisdiction. It can also cross-check police report facts against FNOL details, ISO claim reports, repair estimates, and medical bills to catch inconsistencies that might bolster recovery or trigger SIU review.

Quantified business impact for Auto Claims Adjusters

Clients using Doc Chat report dramatic productivity gains. Routine police report analysis that consumed hours drops to minutes, with complex multi-vehicle crashes processed in under an hour. Summaries are consistent, errors plummet, and recoveries are pursued earlier with stronger factual grounding. Results commonly include:

Cycle time reduction on liability and subrogation determinations by 70–90%
Loss-adjustment expense reductions via automation of reading, extraction, and memo drafting
Fewer missed recoveries and reduced leakage as citations and witness corroborations are never overlooked
Happier adjusters who spend more time strategizing and negotiating and less time retyping

These improvements echo the broader patterns captured in Reimagining Claims Processing Through AI Transformation, where Nomad clients cut reading time by orders of magnitude while improving accuracy and consistency.

Security, governance, and explainability

Doc Chat is built for regulated insurance environments. Nomad Data maintains rigorous security controls, including SOC 2 Type 2 practices, and provides document-level traceability of all outputs. Each answer links back to its exact source page. This defensibility is key for Auto Claims Adjusters, SIU teams, and litigators who must justify decisions to internal auditors, regulators, or courts. Data stays under your control and can be deployed to meet your internal compliance requirements.

Why Nomad Data is the right partner

Nomad Data is not just software — it is a partner. The Nomad Process trains Doc Chat on your playbooks, document types, and jurisdictional standards to deliver a personalized solution. You receive white-glove service from a team that specializes in translating unwritten rules into AI logic, as described in Beyond Extraction — Why Document Scraping Isn’t Just Web Scraping for PDFs. Implementation is measured in days, not quarters: most teams go live in 1–2 weeks, often beginning with out-of-the-box drag-and-drop usage before integrating with claim systems via modern APIs. As adoption deepens, Doc Chat evolves with your needs, from basic police report extraction to advanced portfolio analytics.

Implementation timeline and change management

Standing up Doc Chat for police accident report extraction typically follows this path:

Week 1 — Discovery of your subrogation playbook, jurisdictions, and output formats; sample files loaded; initial presets configured for police reports, state crash forms, and witness statements.

Week 2 — Validation on live claims; feedback cycles tune outputs and question templates; initial users start with drag-and-drop; optional integration work begins with your claim platform.

From there, we expand the footprint to Commercial Auto and GL & Construction nuances, add policy and contract analysis for risk transfer, and standardize demand automation. This phased approach ensures quick wins for Auto Claims Adjusters while laying groundwork for broader automation.

Frequently asked questions from Auto Claims Adjusters

Does Doc Chat replace adjuster judgment? No. Doc Chat handles reading, extraction, and first-draft reasoning. Humans make determinations, negotiate, and decide on recovery strategy.

What if my state’s crash form is idiosyncratic? Doc Chat is trained on variability across states and normalizes outputs. Where unique, we configure jurisdiction-specific parsing and inference rules.

Can it draft notices and demands? Yes. It pre-fills your templates with extracted facts, citations, and witness references, accelerating correspondence while preserving your voice and standards.

How does it perform on handwritten witness statements? Doc Chat handles a wide range of scans. When handwriting is illegible, it flags uncertainty with page citations for quick human review.

What about SIU? Doc Chat surfaces discrepancies and patterns across police reports, FNOL, medical bills, and repair estimates, alerting SIU when appropriate.

How Doc Chat aligns with your KPIs

For Auto Claims Adjusters and their managers, performance is ultimately measured in cycle time, accuracy, recovery dollars, and customer satisfaction. Doc Chat accelerates the slowest part of the process — reading and data entry — while raising accuracy and consistency. The end result: faster liability calls, earlier pursuit of recovery, and fewer missed opportunities. It also reduces adjuster burnout by removing the least engaging parts of the job and letting professionals focus on judgment and negotiation.

From pilot to scale: practical steps

Most teams start with a narrow pilot focused on police accident reports and state crash forms in 1–2 jurisdictions. Within the first week, Doc Chat is tuned to your output formats and question styles. Adjusters validate results against known files — a method that quickly builds trust as described in the GAIG experience. Once validated, expand to Commercial Auto and GL & Construction overlap cases, add witness statements, and enable demand drafting. Over time, connect Doc Chat to your claim system to automatically attach outputs, create tasks, and trigger notices.

Putting it all together

Subrogation success is a race against time and inconsistency. Police accident reports, state crash forms, and witness statements hold the answers, but manual extraction cannot keep pace with today’s file counts and complexity. Doc Chat delivers the intelligence layer Auto Claims Adjusters need to transform reading into action: extract the facts, infer liability, draft next steps, and move. If your team is searching for the best tool to automate accident report analysis or a way to automate subrogation with police report processing, Doc Chat by Nomad Data provides a proven, defensible path forward.

Resources and next steps

Learn more about Doc Chat’s insurance-specific capabilities and how adjusters at Great American Insurance Group accelerated complex claims with page-level citations and instant answers:

Reach out to see a targeted demo using your police accident reports, state crash forms, and witness statements. In most cases you can be live within two weeks, with subrogation-ready outputs tuned to your formats and jurisdictions.