Subrogation plays a critical role in insurance operations, enabling carriers to recover losses from third parties responsible for damages. Traditional processes often involve manual review of claim files, inconsistent prioritization, and delayed recovery actions. These inefficiencies lead to missed opportunities and higher operational costs. To overcome these challenges, the company adopted Decisions to automate predictive subrogation processes with rules-driven logic and machine learning, ensuring high-value cases were prioritized for recovery.
Challenge
Before adopting Decisions, the company experienced:
- Manual claim reviews that slowed the recovery process.
- Limited predictive insights into claim recovery potential.
- Inconsistent routing of claims to subrogation teams.
- High administrative costs and missed recovery opportunities.
These challenges limited the organizations ability to maximize recoveries while also straining operational efficiency.
Solution
The insurer implemented Decisions as a predictive subrogation engine, combining automation with machine learning to prioritize recovery opportunities.
- Automated ingestion of claim data from core systems.
- Predictive scoring to assess the likelihood of successful recovery.
- Rules-driven routing to ensure claims were assigned to the appropriate teams based on recovery probability.
- Dashboards to provide managers with visibility into subrogation performance and outcomes.