AI Agentic Workflow for Police Report Retrieval and Summary

Manual follow-up with law enforcement portals and data entry of report details typically adds 3-7 days to the initial claims investigation. An automated workflow retrieves reports via API or secure portal scraping within minutes of a claim being filed, allowing liability assessment to begin immediately. This directly accelerates time-to-resolution, especially for claims where the police report is the primary liability determinant.

Adjusters or intake staff manually transcribe key facts—parties, VINs, citations, officer narrative—from PDFs into claim systems, a repetitive task prone to typos and omissions. LLM agents extract this data with structured output, populating claim fields directly via API. This eliminates a high-volume administrative task, improves data quality for downstream processes, and frees staff for higher-value customer interaction and complex case analysis.

Manual review can miss subtle inconsistencies. An automated workflow uses the LLM agent to analyze the extracted narrative against the claimant's initial statement, flagging contradictions in location, time, or involved parties. It can also highlight the absence of a report where one is expected or citations issued to the claimant. These red flags are routed to the Special Investigations Unit (SIU) immediately, strengthening early fraud defenses.

During catastrophe events or peak periods, the volume of police report requests can overwhelm staff capacity, creating a bottleneck. An autonomous agent scales elastically, handling thousands of concurrent retrieval and processing tasks without delay. This operational leverage allows the claims operation to maintain cycle time standards and service levels during volume spikes without proportional increases in operational headcount.

Manual processes leave reports as unstructured PDFs in a document management system. The automated workflow parses each report into a structured, normalized data object (JSON) linked to the claim. This creates a searchable repository of liability indicators, vehicle details, and officer observations, enabling portfolio-level analytics on accident causes, high-risk intersections, or common contributing factors for proactive risk management.

The combined savings from reduced manual labor, faster cycle times, and improved early triage directly lower the cost to settle a claim. By automating a predictable, high-frequency task, you convert a variable operational cost (staff time) into a fixed, declining technology cost. This LAE reduction flows directly to the combined ratio, improving the fundamental economics of the claims operation.

This agent handles secure, programmatic access to fragmented law enforcement databases and portal APIs (e.g., LexisNexis Police Reports, CRASH, or regional systems). It manages authentication, session tokens, and structured query submission using the claim's incident details (date, location, parties). The architecture includes robust error handling for portal downtime and fallback logic to trigger manual retrieval requests, ensuring no claim stalls due to integration failures.

Retrieved reports arrive as PDFs, scanned images, or semi-structured text. This agent orchestrates OCR for images and uses a Retrieval-Augmented Generation (RAG) pipeline over internal claim guidelines and legal glossaries to understand context. It chunks the document, creates embeddings for key sections (narrative, diagrams, codes), and prepares a queryable knowledge base for the summary agent, ensuring the LLM grounds its output in the source document.

A specialized LLM agent extracts and normalizes critical entities: involved parties (names, addresses, insurance), vehicle details (VIN, make/model), officer observations, citations issued (code, description), and a distilled narrative of events. It highlights potential liability indicators (e.g., 'failed to yield,' 'contributory negligence') and populates structured claim fields via API. The agent includes confidence scoring for each extracted fact, routing low-confidence extractions for human review.

This component is the workflow's brain, built on a framework like LangGraph. It matches the summarized report to the correct claim file using fuzzy matching on parties, date, and location. It validates data consistency (e.g., does the vehicle description match the policy?), flags discrepancies for adjuster review, and routes the completed summary package into the core claims system (e.g., Guidewire, Duck Creek). It manages the state of each report through retrieval, extraction, and integration phases.

For regulated claims handling, every automated action requires a defensible audit trail. This agent immutably logs the source report, retrieval timestamp, extraction outputs with confidence scores, the human reviewer (if any), and the final data written to the claim file. It generates compliance-ready reports and integrates with legal hold systems, ensuring the workflow meets stringent regulatory and e-discovery requirements for claims documentation.

Deployment involves containerized agents (e.g., Docker, Kubernetes) communicating via a message broker (e.g., RabbitMQ). Integration points include: the Policy Administration System for claim context, Document Management for report storage, and CRM/Portal for status updates. A human-in-the-loop dashboard allows adjusters to review low-confidence extractions, correct errors, and provide feedback that retrains extraction models, creating a closed-loop system that improves over time.

The workflow must connect to disparate law enforcement databases, which often lack modern APIs. This requires building secure, credentialed connectors to state and local portals (e.g., CrashDocs, LexisNexis Police Reports), handling session management for web portals, and parsing PDF or XML response formats. For jurisdictions without digital access, the architecture includes OCR pipelines for mailed or faxed reports, introducing latency that must be managed in SLAs.

A specialized LLM agent, using a Retrieval-Augmented Generation (RAG) pattern over report schemas, extracts and normalizes key entities: parties, VINs, citations, officer narrative, and diagram references. The agent must handle inconsistent formatting, legalese, and missing fields, outputting structured JSON. Confidence scoring for each extracted field determines if the summary is auto-populated into the claim file or routed for human review, preventing garbage-in, garbage-out scenarios.

Upon retrieval, a matching agent correlates the report to the open claim using fuzzy logic on date, location, and party names. A separate analysis agent scans the extracted narrative and codes for potential liability indicators (e.g., 'failed to yield,' 'disputed point of impact') and flags discrepancies with the claimant's statement. These signals are surfaced to the adjuster's dashboard, turning raw data into actionable intelligence for faster investigation.

Every report request, data access, and field edit is logged to an immutable audit trail with agent rationale. This is critical for regulatory compliance (e.g., handling PII) and litigation readiness. Workflow controls include automated redaction of sensitive PII not required for adjustment and strict access logging to meet CJIS-like standards where applicable. The system must produce a defensible record of how the report was obtained and used.

The workflow must gracefully degrade. Exceptions—like a failed portal login, an unreported case number, or a low-confidence extraction—are routed to a human-operated queue within the claims system. Adjusters or a dedicated support team can intervene to manually retrieve or review. The architecture uses LangGraph or similar for state management, ensuring the workflow pauses, notifies, and resumes after manual resolution without data loss.

Manually requesting and summarizing a police report takes an adjuster 45-90 minutes over 3-7 days of waiting. This automation compresses that to minutes of automated effort, shaving 2-5 days off the investigation phase. The ROI comes from reallocating adjuster time to complex tasks, reducing rental and storage costs accelerated by faster liability decisions, and improving data accuracy for downstream subrogation and fraud analysis.