Claims represent an insurance company’s biggest expense, with claims payouts and loss-adjustment expenses accounting for up to 80 percent of an insurance company’s revenue. One way to reduce these expenses is through claims recovery, but unfortunately opportunities for claims recovery are often obscured by the sheer volume of claims data available or are missed because the indicator for a possible recovery is hidden in the claims narrative.
The growing complexity of the management of subrogation rules and regulations, combined with understaffed teams, has led to a steady increase in time-consuming investigations and ineffective recovery processes, resulting in missed opportunities for recovery that could have implications for an insurer’s overall profitability. Loss expenses have risen, and poor subrogation rates result in higher premiums that can reduce new business sales and lower your retention rates.
Subrogation and salvage are two significant parts of the claims process that can enable insurers to recover loss costs and have a positive impact on their profitability. Data analysis can help insurers:
- Minimize the number of missed recovery cases.
- Detect all cases to be recovered earlier in the process, thanks to automatic alerts that are generated at the earliest possible opportunity.
- Reduce investigation time and costs by enabling investigators to prioritize and triage potential recovery opportunities.
- Gain a deeper understanding of claims by using predictive modeling and text mining techniques to analyze both structured and unstructured claims data.
Data analysis helps you achieve a rapid, sustainable return on all of your recovery opportunities. You can reduce loss costs, ensure greater customer satisfaction and enhance customer loyalty.