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Stuart Rose, SAS
Stuart Rose, SAS
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Claims Recovery Optimization: Turning a Cost Center into a Money Generator

Insurance companies are turning to analytics to improve their loss ratio by optimizing the claims recovery process through data mining and text mining techniques.

Stuart Rose
Stuart Rose, SAS
Claims is arguably the most important of all insurance business processes not just because of its effect on customer satisfaction but also because of its financial impact. Most parts of the claims process involve making claims payments to first-party or third-party claimants. These claims payments and the related loss-adjustment expenses typically account for up to 80 percent of an insurer’s operational expenses. However claims need not be just an enormous cost center; there is one part of the claims process that can actually generate money for a carrier: claims recovery.

To a significant extent, claims recovery represents an unrealized opportunity for insurers. For example, an estimated average of 5 percent of claims that should go to subrogation don’t. For an insurance company with $200m in potential subrogation opportunities, this could mean an addition $10 million in revenue.

Opportunities for claims recovery, such as through salvage and subro, are often obscured by the sheer volume of claims data available. For example, last year in the United States there were approximately 6 million auto claims and a staggering 5 million workers compensation claims. Plus much of the claims data, around 75 to 80 percent, is considered unstructured data, e.g., adjuster notes and medical records, making it difficult to identify likely subrogation opportunities. Successful claims recovery requires advanced technology, like analytics, to maximize recovery dollars.

The management of subrogation rules and regulations has become quite complex over the years. This complexity, together with overworked and understaffed teams, has led to a steady increase in time-consuming investigations and ineffective recovery processes. Unfortunately, this has resulted in missed recovery opportunities that could have considerable implications for an insurer’s overall profitability, as suggested above.

While the primary impact of an improved claims recovery process impacts the carrier’s combined ratio, a secondary impact of increased and more-timely subrogation recoveries is the positive impact on customer satisfaction and retention rates. Because almost everything a company collects through subrogation directly boosts its bottom line, good subrogation results can help a carrier provide additional lowered premium rates, retaining and attracting customers. Timely return of deductibles also results in a happy customer, motivating positive word of mouth. In the age of social media, word of mouth—whether positive or negative—travels faster than ever.

The claims recovery process is typically time-consuming and labor-intensive, involving multiple systems, and often outdated technology. The pressure to settle claims faster with greater transparency means that many insurers don’t have time to make a decision on the claims with the highest potential for recovery. With all these challenges, insurance companies are turning to analytics to improve their loss ratio by optimizing the claims recovery process.

By using data and text mining techniques, property and casualty insurers have:

- Minimized the number of missed recovery cases by recognizing known and unknown subrogation indicators in the claims information;

- Increased the chances of recovery by detecting all case to be recovered earlier in the process and generating automatic alerts;

- Reduced investigation time and costs by prioritizing and triage potential recovery opportunities; and

- Analyzed both structured and unstructured claims data to gain a better understanding of the loss.

One leading European insurer was able to improve its recovery rate by over 4 percent, representing millions of dollars per year to its bottom line.

As insurance becomes a commodity, insurance carriers need to consider how they can differentiate themselves from their competitors. Adding analytics to the claims recovery process can deliver a measurable ROI with “new revenue”, cost savings and ultimately increased profits. Claims recovery analytics will also deliver intangible benefits, such as improved customer satisfaction, resulting in a win-win arrangement for both the customer and the insurance carrier.

About the Author: Stuart Rose is global insurance marketing manager at Cary, N.C.-based SAS. Rose, a 20-year veteran of the insurance industry, began his career as an actuary. He has worked for a global insurance carrier in both its life and property divisions and has worked for several software vendors, where he was responsible for marketing, product management and application development. He has driven successful development and implementation of enterprise systems with insurance companies in the U.S., the U.K., South Africa and Continental Europe.

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