When it comes to claims processing, insurers are obsessed with cycle time.
They count the days it takes to make a claim adjudication decision, the minutes it takes to complete the loss intake process, and the seconds it takes to process a transaction. Especially in high-volume environments, time is money.
In the wisdom of insurance claims executives, faster claim payments generally equate to better customer satisfaction and loyalty. Anything that slows the process is burdensome and costly. Insurance companies are always looking for ideas on how to improve or optimize the claims process.
Predictive insurance claims processing, or claims analytics, is the process of analyzing structured and unstructured data at all stages in the claims cycle to make the right decision, at the right time, for the right party.
Here are four areas in which applying analytics to the claims process can have the biggest effect:
1. Fraud analytics
Fraud is a large and growing problem for the insurance industry. Most research estimates that about 10% of insurance claims are fraudulent and cost the insurance industry billions of dollars. To combat claims fraud, insurance companies should implement a real-time or near-real-time analytical engine that calculates the propensity for fraud at each stage of the claims life cycle.
The fraud analytical engine must use a combination of techniques, including business rules, predictive modeling, text mining, database searches and exception reporting. In addition, insurers should consider network link analysis technology, which analyzes all historical claims to quickly discover organized fraud rings that might otherwise take months or years to identify and prevent.
2. Recovery optimization
Recovery optimization scores claims at each stage in the claims lifecycle based on known subrogation characteristics, identifying unknown characteristics and optimizing associated activities. By using text analytics, insurers can analyze adjuster notes or other unstructured data to find phrases that typically indicate a subrogation case. Pinpointing likely subrogation opportunities earlier, insurers maximize loss recovery and ultimately reduce loss expenses.
3. Settlement optimization
Bringing consistency to the claims settlement process is an important objective — especially for claims managers who are pressured to settle faster, with transparent fairness, while using fewer resources and reducing loss-adjustment expenses.
The first ongoing problem with managing claims leakage comes down to one simple thing: Insurers have no effective way of predicting the size and duration of a claim when it is first reported. But accurate loss reserving and claims forecasting is essential, especially in long-tail claims like liability and workers’ compensation. Analytics can more accurately calculate the loss reserve by comparing a loss with similar claims. Then, whenever the claims data is updated, analytics can reassess the loss reserve.
The second issue is to assign claims to the right resources right from the start at first notice of loss and by ensuring that priority claims receive priority treatment. By implementing data mining techniques to cluster and group loss characteristics (such as loss type, location and time of loss, etc.), claims can be scored, prioritized and assigned to the most appropriate adjuster based on experience and loss type. High severity and more complex cases are assigned to the most qualified adjusters, while low-exposure claims are channeled to less experienced adjusters. In some cases, they can even be automatically adjudicated and settled.
4. Litigation optimization
A significant portion of a company’s loss expense ratio goes to defending disputed claims. Every insurer can relate to the typical horror story claim where the passenger of an auto accident broke a finger and walked away with a $250,000 settlement. With litigation optimization, insurers can use analytics to calculate a litigation propensity score.
Claims that involve an attorney often double the settlement amount and significantly increase an insurer’s expenses. Analytics can help determine which claims are likely to result in litigation. Those claims can be assigned to more senior adjusters who are likely to be able to settle the claims sooner and for lower amounts.
As insurance becomes a commodity, carriers need to consider how they can differentiate themselves from competitors. Adding analytics to optimize the claims lifecycle can deliver a measurable ROI with cost savings and increased profits; just a 1% improvement in the claims ratio for a $1 billion insurer is worth more than $7 million on the bottom line. Claims optimization also delivers intangible benefits, such as improved customer satisfaction. And that is a win-win arrangement for both the customer and the insurance carrier.
Stuart Rose is global insurance marketing manager at Cary, N.C.-based business analytics software and services company SAS.