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

Speeding Up Subrogation: Automated Extraction from Police Accident Reports

Auto, Commercial Auto, and General Liability & Construction claims teams are under constant pressure to recover dollars swiftly and accurately. For the Subrogation Specialist, that pressure is often concentrated in one place: the police accident report. These documents are essential for pinpointing liability, identifying the responsible parties, and initiating timely recovery. But they are also sprawling, inconsistent, and time-consuming to mine for facts—especially when every state crash form, officer narrative, diagram, citation, and witness statement looks different. The challenge is clear: manual review is too slow and too variable to keep up with today’s claim volumes and cycle-time expectations.

Nomad Data’s Doc Chat removes that bottleneck. Purpose‑built for insurance, Doc Chat automatically ingests full claim files, including police accident reports, state crash forms, and witness statements, and extracts the fields Subrogation Specialists need to assess liability and pursue recovery in minutes, not days. With real-time Q&A, page-level citation links, and outputs tailored to your subrogation playbook, Doc Chat lets teams immediately see who’s at fault, what statutes were cited, where coverage and policy details appear, and how to contact adverse carriers—without manual scrolling. Learn more about how it works here: Doc Chat for Insurance.

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

Subrogation Specialists are the recovery engine across Auto, Commercial Auto, and General Liability & Construction. Whether it’s a rear-end collision involving a personal vehicle, a lane-change crash with a tractor-trailer, or a jobsite incident involving construction equipment on public roads, the starting point for recovery is the same: extract the facts from documentary evidence and establish the case for liability. But the nuances across lines of business and jurisdictions create a tangle of complexity:

Format fragmentation: State crash forms vary widely—Texas CR‑3, Ohio OH‑1, Florida HSMV 90010S, California CHP 555, New York MV‑104A, and many more. Each embeds location of damage, weather factors, vehicle dynamics, impairment indicators, and citations in different layouts.
Inconsistent terminology: Officer narratives, checkboxes, and hand-written notes use non-standard language. A single factor (e.g., failure to yield) can be captured three different ways across forms.
Multi-party exposures: Commercial Auto and GL/Construction claims often involve fleets, subcontractors, rental agreements, or jobsite traffic control plans, complicating insured-versus-adverse identification.
Evidence sprawls across files: Witness statements, dashcam footage, photos, ISO claim searches, prior loss run reports, repair estimates, and demand letters are often stored as separate PDFs or emails, creating context gaps.
Deadlines and statutes: Notice requirements, evidence preservation, and statute of limitations windows require fast action. Delay can mean losing recovery altogether.

For Subrogation Specialists, the skill is not merely reading; it’s synthesizing: connect the diagram to the narrative, link the VIN to the plate to the policy, match citations to probable negligence, and spot adverse carriers fast. Doing all that quickly, accurately, and at scale is where manual processes break down.

How the process is handled manually today

Today’s manual process typically looks like this:

The adjuster or Subrogation Specialist receives a police accident report and supporting materials (state crash forms, officer narratives, witness statements, photos, repair estimates, medical notes, FNOL data). They manually scan for driver names, VINs, license plates, insurance details, adverse carriers, citations, weather/road factors, and the officer’s primary contributing factors. They reconcile inconsistencies between the narrative and the diagram. They check ISO claims reports, pull prior losses, and look for potential comparative fault. They then copy/paste key facts into the claim system and compose an initial notice to the adverse carrier, often after chasing missing data via emails and calls to police records divisions.

Every step invites variation: two reviewers produce two different summaries. Reviewer fatigue can miss a citation or misread a checkbox. In a high-volume Subrogation unit, this manual approach leads to backlogs, longer days-to-recovery, and leakage from missed opportunities. It also ties up talent on data entry instead of negotiation and recovery strategy.

What subrogation pros actually need from AI

To truly help Subrogation Specialists, AI must read like an experienced practitioner and deliver consistent, playbook-grade outputs. Beyond basic OCR, the solution should infer and connect concepts across disjointed content. In other words, it must “think like” a subrogation expert—not just scrape text. As we’ve argued in our perspective on document intelligence, document scraping is about inference, not location. For subrogation, that means automatically compiling:

Parties and roles: drivers, passengers, pedestrians, vehicle owners, employers, contractors/subcontractors.
Identifiers: VIN, license plate, driver’s license, policy numbers, unit numbers, DOT numbers for Commercial Auto.
Adverse carrier and contact: insurer name, NAIC, policy number if listed, or a confidence-ranked best match from context.
Citations and statutes: code sections, contributing factors, impairment notes, and any associated test results.
Liability indicators: point of impact, vehicle movement, right-of-way, traffic controls, weather/road conditions, and diagram inferences.
Comparative fault notes: factors that could reduce or offset recovery depending on jurisdiction (e.g., lane change without signal vs. tailgating).
Damages snapshot: damages noted, tow/total indicators, preliminary severity, injured parties and treatment references.
Missing information checklist: pages or forms not present, illegible sections, missing witness contact details, absent attachments referenced in narrative.

Finally, AI should generate a clean, standardized subrogation summary that feeds your claim system fields and produces an editable outbound template to the adverse carrier. Anything less creates a new kind of rework.

How Doc Chat automates police report processing for subrogation

Doc Chat by Nomad Data is a suite of purpose-built, AI-powered agents that ingest entire claim files—thousands of pages at a time—and deliver consistent, defensible outputs. For subrogation, Doc Chat reads police accident reports, state crash forms, and witness statements end-to-end, extracting exactly what your team needs and linking each fact to its source page for instant verification. Adjusters can simply ask, “Who was cited?” or “List all factors indicating failure to yield,” and receive answers in seconds with links back to the exact form sections and narrative lines. This is real-time Q&A across massive document sets, designed for insurance workflows.

Key capabilities Subrogation Specialists rely on include:

Automated extraction: Doc Chat captures names, roles, VINs, plates, policy numbers, citations, weather/road factors, damages, and witness contacts from diverse state crash forms and narratives.
Inference across formats: It reconciles discrepancies between checkboxes and narratives, correlates diagrams to contributing factors, and flags comparative fault elements by jurisdiction.
Standardized subro summary: Outputs are tailored to your team’s structure—e.g., “Parties,” “Liability Indicators,” “Citations & Codes,” “Damages,” “Adverse Carrier,” “Recovery Strategy,” with confidence scores and missing-info checklists.
Immediate outreach readiness: Generate a pre-populated notice of subrogation to the adverse carrier with embedded facts and citations, ready for your final review.
Bulk processing at scale: Ingest entire backlogs of police reports and related attachments; convert days of manual review into minutes. As discussed here, Doc Chat can process approximately 250,000 pages per minute (source).
Page-level explainability: Every extracted field links to the source page, giving QA, compliance, reinsurers, and counsel the defensibility they require (see how GAIG leverages page-level citations).

Because Doc Chat is trained on your subrogation playbooks and standards, it mirrors how your best people work—just faster and more consistently. See the product overview: Doc Chat for Insurance.

AI to extract info from police reports for subrogation

If you’re actively searching for AI to extract info from police reports for subrogation, Doc Chat is purpose-built for the job. Ask natural-language questions—“Which driver had right-of-way?” “Show impairment indicators.” “Summarize witness contradictions.”—and get instant answers with citations. Then export structured fields to your claim system and auto-generate your first contact to the adverse carrier.

From FNOL to recovery: automating the subro workflow

Doc Chat is not just extraction; it is end-to-end workflow acceleration. From FNOL, the platform can triage files for subrogation potential based on early indicators in police crash reports and initial statements. It flags whether adverse coverage information is present, identifies jurisdictions for comparative fault rules, and compiles a task list for missing documentation. As additional documents arrive (e.g., supplemental reports, additional witness statements, demand letters, repair invoices), Doc Chat updates the summary, recalculates confidence in liability assessments, and revises outreach templates automatically.

For complex Commercial Auto and GL/Construction matters, Doc Chat highlights employer/owner relationships, DOT numbers, subcontractor references, traffic control plan mentions, and any third-party entities noted in the narrative. It also surfaces potential product or premises liability cross-exposures where relevant, allowing Subrogation Specialists to coordinate with General Liability or risk transfer teams when appropriate.

Business impact: time savings, cost reduction, accuracy, and recoveries

The operational and financial benefits of automating police report processing are direct and compelling for Subrogation Specialists:

Time savings: Reduce report review from hours to minutes. Over a month, a single specialist can reclaim dozens of hours that shift from low-value reading to high-value negotiation and recovery.
Cost reduction: Lower loss-adjustment expense by compressing manual touchpoints. Avoid overtime during surge events. Scale subro operations without adding headcount.
Accuracy and consistency: Doc Chat reads page 1,500 with the same attention as page 1, eliminating fatigue-driven errors. It enforces standardized outputs across your team, improving audit scores and defensibility.
Faster cycle times: Accelerate first contact with adverse carriers and shorten days-to-recovery by surfacing facts instantly and generating outreach-ready templates.
Higher recoveries and reduced leakage: Fewer missed citations or liability indicators. Stronger, better-documented demands backed by page-level citations produce better negotiation leverage.

These outcomes align with broader insurance AI gains we’ve documented across claims. In complex-file environments, our clients see multi-week reviews reduced to under an hour, while maintaining full transparency and auditability (read how carriers reimagine claims with AI). By eliminating the administrative burden, Subrogation Specialists can focus on strategy and closing dollars.

Best tool to automate accident report analysis: why Nomad Data

Insurers searching for the best tool to automate accident report analysis should consider three criteria: speed at scale, inference quality, and explainability. Doc Chat leads on all three, plus it is tailored to subrogation workflows:

Scale without friction: Ingest entire claim files—thousands of pages at a time—and get results in minutes. No more throttling due to file size or format diversity.
Inference over simple extraction: As we note in Beyond Extraction, the value lies in recreating human reasoning—linking diagrams to narratives, inferring right-of-way from options selected and text written, and identifying comparative fault markers.
Real-time Q&A with page links: Ask Doc Chat questions and receive instant answers with clickable citations to the exact page and paragraph—critical for negotiations and audits. See how a major carrier uses this in practice: GAIG x Nomad.
Personalized to your playbooks: The Nomad Process trains Doc Chat on your documents and standards, so outputs match your subro templates, claim fields, and reporting needs.
Security and compliance: Enterprise-grade controls and SOC 2 Type 2 posture. Answers always come with traceability—no black boxes.
White-glove partnership: You’re not buying generic software. You’re gaining a partner who co-creates with your subrogation leaders to deliver measurable recovery impact.

Bottom line: Doc Chat is the insurance-first AI that marries speed, depth, and defensibility—the ingredients your subrogation program needs to scale recoveries.

Implementation: live in 1–2 weeks with white-glove service

Doc Chat is designed to deliver value fast. Most subrogation teams are live in 1–2 weeks thanks to a pragmatic rollout that starts with your most common police report formats and expands from there. A typical plan looks like this:

Week 1 – Discovery and alignment: Identify top report types (e.g., Texas CR‑3, CHP 555), target fields, current templates, and system fields (Guidewire, Duck Creek, Origami, or in-house). Share sample redacted files and your subro playbook.
Week 1 – Preset build: We configure Doc Chat presets to your summary format (Parties, Liability, Citations, Damages, Missing Items, Recovery Strategy). We codify comparative fault notes by jurisdiction if applicable.
Week 2 – Validation loop: Your Subrogation Specialists run side-by-side tests on recent files, confirming accuracy. We adjust outputs, field mappings, and confidence thresholding.
Go-live – Minimal IT lift: Start with a drag-and-drop interface; add API integration to your claim system later for auto-population. Training takes hours, not weeks.
Ongoing – Co-creation: We iterate on edge cases (multi-vehicle pileups, hazmat incidents, jobsite traffic control disputes) and expand to more states and formats.

This white-glove approach ensures quick wins while laying the groundwork for full-scale automation. For teams who also handle medical packages or demand letters, Doc Chat’s multi-document capabilities deliver even broader efficiency gains (learn about ending file review bottlenecks).

Use cases across Auto, Commercial Auto, and GL/Construction

Auto: Rear-end collision at a signal

Doc Chat extracts driver roles, identifies the cited party and statute (e.g., failure to maintain assured clear distance), correlates the diagram with the narrator’s sequence, and flags any comparative fault notes (e.g., sudden stop without cause). The result is a ready-to-send subrogation notice with page citations to strengthen negotiations.

Commercial Auto: Tractor-trailer lane change

When a semi-truck changes lanes into a passenger vehicle, Doc Chat surfaces DOT numbers, unit IDs, employer/owner relationships, witness contradictions, and weather/visibility factors. It highlights potential shared fault, organizes carrier contacts, and recommends a tailored outreach script for complex multiparty negotiations.

GL/Construction: Jobsite equipment entering public roadway

In construction incidents, Doc Chat captures subcontractor references, traffic control plan mentions, and third-party vehicles impacted. It links narrative details to the diagram and notes signage/flagging references, helping your Subrogation Specialist coordinate with GL risk transfer and pursue recovery against responsible parties.

Multi-vehicle pileup with unclear narratives

Doc Chat harmonizes conflicting witness statements, compares timelines, and highlights contradictions with citations. It proposes an investigative checklist for missing statements or supplemental reports, compressing weeks of back-and-forth into a single review session.

Automate subrogation with police report processing

Teams seeking to automate subrogation with police report processing benefit most when extraction is paired with action. Beyond data capture, Doc Chat auto-generates:

Adverse carrier outreach: Pre-populated letters/emails with parties, citations, and a concise liability narrative, including page references.
Task lists and reminders: Follow-ups for missing attachments, witness contact attempts, or police records requests.
Claim system updates: Bulk export or API synchronization for fields such as parties, vehicle details, fault assessment, and next steps.
Negotiation prep packets: A single PDF containing the subro summary plus stamped source pages, ready for counsel or recovery vendors.

These capabilities move your team from “read and retype” to “review and recover.”

Security, explainability, and trust

Data protection and defensibility are non-negotiable. Doc Chat delivers page-level citations for every answer and maintains an auditable trail that compliance, legal, and reinsurers can review. Our enterprise posture includes SOC 2 Type 2 controls, and outputs are tuned to your governance standards. As we’ve written about data entry automation more broadly, extraction from documents is a domain where large language models perform with high precision when asked to find specific information in provided files (read AI’s Untapped Goldmine).

Just as importantly, Doc Chat keeps humans in the loop. Think of it as a highly capable junior analyst working at machine speed, with your Subrogation Specialists providing the final judgment. This model aligns with best practices we’ve seen across complex claims environments (more on transforming claims with AI).

KPIs: measuring subrogation success with Doc Chat

Successful programs keep score. Doc Chat equips leaders with clear metrics:

Cycle time: Average days from receipt of police report to first adverse contact and to first dollar recovered.
Throughput per FTE: Police reports processed per specialist per week and recovery dollars per hour.
Hit rate and severity: Percentage of files with subro potential identified and average recovery per successful file.
Quality and compliance: Audit pass rates, page-citation completeness, and variance between reviewers reduced via standardized outputs.
Leakage reduction: Decrease in missed subro opportunities tied to late or incomplete analysis.

These KPIs translate directly to improved loss ratios and lower loss-adjustment expense while raising team morale by eliminating repetitive, low-value work.

Real-world confidence: page-level transparency in action

One reason carriers adopt Doc Chat quickly is the trust built through transparent answers. In our work with Great American Insurance Group, adjusters could ask complex questions and receive instant answers with a clickable link to the source page—allowing fast verification without manual scrolling. That combination of speed and verifiability changed daily rhythms and accelerated cycle times (watch the GAIG replay).

For subrogation, that same transparency underpins negotiations. When the adverse carrier asks, “Where did you get that?” you can point to the exact line and page, strengthening your position and compressing disputes about facts into minutes, not days.

Beyond police reports: full-file intelligence

While this article focuses on police accident reports, the reality of subrogation is broader. Doc Chat also ingests demand letters, medical records, repair estimates, invoices, ISO claim reports, loss run reports, and correspondence. It can produce medical and damages summaries, list procedures and providers, and cross-check damages claims against the incident facts in the police report. This holistic view improves recovery strategy and reduces leakage across the claim. When you’re ready to expand beyond crash forms, Doc Chat is already built for the rest of the file.

Change management and adoption

Adopting AI in subrogation is as much about people as it is about technology. We recommend starting by running Doc Chat on a set of files your Subrogation Specialists know intimately. Seeing the system surface the right facts with page citations builds immediate trust—and often converts skeptics into champions. From there, formalize your presets, integrate with your claim system, and expand to more document types and jurisdictions. Because Doc Chat standardizes outputs, it also shortens onboarding for new hires and protects institutional knowledge, turning unwritten shortcuts into scalable, teachable processes.

FAQ: what Subrogation Specialists ask us most

How does Doc Chat handle different state crash forms and handwriting?

Doc Chat is trained on diverse police report formats and uses a combination of OCR, NLP, and inference to normalize fields across states. It can read typed and many hand-written sections with high accuracy, and it always provides page-level citation links so reviewers can confirm extracted data instantly.

Can it infer liability, or does it only extract fields?

It does both. Doc Chat extracts measurable fields and also highlights liability indicators across the diagram, narrative, and checkboxes—right-of-way, movement, traffic controls, impairment notes, and citations—along with a structured summary aligned to your comparative fault framework. Your team makes the final determination.

How fast can we go live?

Most subrogation teams launch in 1–2 weeks. We begin with your highest-volume crash forms and expand quickly, with minimal IT lift. Start with a secure, drag-and-drop interface; add API integration when you’re ready.

Will this replace my specialists?

No. Doc Chat eliminates rote reading and data entry, so your specialists can focus on strategy, negotiation, and recovery. It is a force multiplier that increases throughput and improves quality.

What about security and compliance?

Doc Chat is built for enterprise insurance environments with SOC 2 Type 2 controls and full traceability. Every answer is backed by page-level citations and audit trails to support internal QA, reinsurers, and regulators.

The bottom line

Subrogation hinges on speed, accuracy, and leverage. Police accident reports contain the facts you need—but extracting them manually costs time and leaves money on the table. Doc Chat gives Subrogation Specialists in Auto, Commercial Auto, and GL/Construction instant access to the who/what/where/why of liability with page-level transparency, standardized outputs, and workflow-ready exports. If your team is searching for AI to extract info from police reports for subrogation, looking for the best tool to automate accident report analysis, or ready to automate subrogation with police report processing, the next step is simple: see Doc Chat in action.

Discover how quickly your team can move from reading to recovering: Nomad Data Doc Chat for Insurance.