Automation Workflow for Automated Subrogation Potential Identification

By systematically flagging every claim with third-party fault, this workflow converts overlooked liabilities into cash recoveries. It directly reduces net loss payouts, improving the combined ratio by 0.5–2.0 points for carriers with significant auto or property books. The architecture ensures 100% coverage of paid claims, eliminating the manual review gap where 15–20% of recoverable claims are typically missed.

Manual subrogation identification can take weeks, delaying the start of the recovery process and reducing negotiation leverage. This autonomous workflow analyzes liability and payment data within minutes of claim closure, preparing the initial demand package immediately. By shortening the identification-to-demand cycle from weeks to hours, it improves recovery success rates and accelerates cash flow.

Recovery units are high-cost, specialized resources. This workflow acts as a force multiplier, performing the initial screening, evidence compilation, and case packaging. It routes only high-potential, well-documented cases to specialists or external counsel, allowing them to focus on negotiation and litigation rather than discovery. This can effectively double or triple the number of cases each specialist can manage.

Leakage occurs when subrogation opportunities are missed due to adjuster turnover, high caseloads, or process inconsistency. This workflow institutionalizes the recovery process with deterministic rules and ML models that evaluate fault likelihood, statute of limitations, and potential recovery amount. It creates a consistent, auditable operating standard, plugging a major source of financial leakage in claims operations.

The workflow generates a complete audit trail for every decision: which claims were flagged, why, and what evidence was compiled. This is critical for internal audits, reinsurance reporting, and regulatory reviews. By automating and documenting the subrogation identification process, carriers strengthen their compliance posture and create a transparent record of diligent efforts to pursue recoveries.

The agentic architecture, built on frameworks like LangGraph, scales elastically to handle volume spikes from CAT events or seasonal peaks without adding staff. The workflow's performance is not bottlenecked by human review capacity, ensuring recovery identification keeps pace with claim intake. This creates a durable, variable-cost operating model for subrogation that protects margins during high-activity periods.

This core agent reviews completed liability assessments, payment details, and police reports against a rules engine to flag claims with clear third-party fault (e.g., other driver cited, product defect). It assigns a recovery likelihood score (High/Medium/Low) based on evidence strength, jurisdiction, and statutory limits, routing only viable cases forward. Implementation involves integrating with the claims core system, ingesting structured liability decisions, and applying configurable business logic in a service like LangChain.

For high-likelihood cases, this agent autonomously assembles the initial subrogation demand package. It retrieves and collates key evidence from the claim file—police reports, photos, estimates, payment proofs—and drafts a demand letter using templated language populated with case-specific facts. Implementation requires RAG over document stores, integration with systems like Guidewire or Documentum, and secure packaging for external transmission.

This orchestrator agent manages the handoff from automation to human teams. It assigns the prepared case to the appropriate recovery specialist or external counsel based on jurisdiction, amount, and complexity, while simultaneously updating the claim system and triggering task creation in the specialist's workflow queue (e.g., ServiceNow, Salesforce). Implementation is built on a state machine (LangGraph) that handles assignment logic, status sync, and exception routing for unassigned cases.

To accelerate the process, this agent performs automated discovery to identify the at-fault party's insurer. It queries industry databases (e.g., ISO ClaimSearch, state regulatory filings) or parses evidence for policy information, then initiates first contact via secure API or prepares correspondence for the specialist. Implementation involves secure API integrations with data providers and logic to handle missing or conflicting data by escalating to the specialist.

This monitoring agent tracks the status of all subrogation cases initiated by the workflow, ingesting updates from specialist systems to calculate key metrics: recovery rate, cycle time, and dollars recovered. It provides real-time dashboards for managers and feeds data back to improve the flagging agent's scoring model. Implementation requires building a data pipeline from operational systems to a analytics layer, with alerts for stalled cases.

Not all cases are clear-cut. This agent catches exceptions—disputed liability, complex multi-party accidents, jurisdictional ambiguities—and routes them to a senior adjuster or legal review queue with a detailed summary of the complicating factors. It enforces governance by ensuring no automated demand is sent without passing predefined confidence thresholds. Implementation centers on configurable routing rules and integration with case management systems for human-in-the-loop review.

The workflow's primary data source. It ingests finalized liability decisions, payment details, and coverage terms via real-time APIs or event streams from systems like Guidewire ClaimCenter, Duck Creek Claims, or SAP FS-RI. This integration provides the foundational 'who paid for what' data to identify third-party fault and calculate potential recovery amounts. Without this live connection, the system cannot operate on closed claims.

Agents retrieve the evidentiary basis for the liability decision—police reports, witness statements, photos, and assessor notes—from systems like OpenText, SharePoint, or Box. Using RAG (Retrieval-Augmented Generation), the workflow synthesizes this unstructured data to build the initial demand package, automatically attaching key documents to substantiate the recovery claim. This eliminates manual evidence gathering for the recovery specialist.

To initiate recovery, the system must identify the at-fault party's insurer. This requires queries to industry databases like ISO ClaimSearch, LexisNexis Carrier Discovery, or state-specific insurance verification systems. The workflow automates these lookups using the liable party's details from the claim file, ensuring the demand is routed to the correct opposing carrier and preventing dead-end searches.

Flagged cases are automatically created as subrogation matters in specialist systems such as Mitchell Subrogation, STRATIS, or legal matter management platforms. The workflow pushes the synthesized evidence package, calculated demand, and key dates into the case file, assigning it to the appropriate recovery unit or external counsel based on amount and complexity rules. This handoff is critical for closing the automation loop and tracking ROI.

The workflow consumes feeds from the accounts payable system to confirm the exact amount paid for damages related to third-party fault. This financial data is essential for calculating the precise recovery demand. Post-recovery, the system receives confirmation of funds received, automatically closing the loop and updating the claim's financials, contributing to accurate loss ratio reporting.

The logic determining 'recoverability' is not static. The workflow queries a central rules engine (e.g., IBM ODM, Drools) for jurisdictional thresholds and liability statutes. It also analyzes a historical data lake of past recovery outcomes by state, peril, and carrier to predict likelihood of success and recommend settlement strategies. This dependency ensures the system learns and improves its flagging accuracy over time.