To people who do a lot of things online, experience is everything. These days, insurance customers are expecting easy sign-up and quick claims from their insurance providers.  For insurance companies, this means identifying drivers of customer satisfaction and translating them into operational performance improvements. However, this requires deep customer insights and solid analytics.

A big part of insurance today is given through brokers. When customers make contact directly with an insurance company for the first time, they can make a claim. That is why insurers can find it hard to build a relationship with their customers. Insurance companies will want to ensure their customers get positive claims experience that can be improved with predictive analytics.

Here’s how analytics can make a big difference with insurance claims data and improve insurance customer experience:

Spotting Fraud

Spotting fraud is important for any insurance company before they can make a hefty payout. Predictive analysis makes use of a combination of rules, text mining, modeling, exception report, and database searches to identify fraud effectively sooner at every stage of the claims cycle.

Pinpointing Subrogation Opportunities Early

Usually, subrogation opportunities get lost in big volumes of data in the form of medical records, adjuster notes, or police records. But, unstructured data can be sought through to find phrases that often indicate a subrogation case. The ability to pinpoint subrogation opportunities lets insurance companies maximize loss recovery while minimizing loss expenses.

Optimizing the Limits for Instant Payouts

Usually, insurance companies implement fast-track processes to settle claims instantly. However, this can be costly when the company overpays. Analyzing claims and claim histories allows insurers to optimize the limits for instant payouts. Also, insurance data analytics shorten claims cycle times for reduced labor costs and higher customer satisfaction.

Calculating Loss Reserve Accurately

Once a claim is reported, predicting its duration and size is almost impossible. However, accurate reserving of loss and forecasting claims is necessary, especially in long-tail claims such as liability and workers’ compensation. This is possible through analytics by comparing a loss with similar claims. And when insurance claims data is updated, analytics can re-evaluate the loss reserve, helping insurance providers understand how much money they need to meet future claims.

Calculating a Litigation Propensity Score

A considerable part of the loss adjustment expenses ratio of an insurance company goes to defending disputed claims. Analytics can be used for calculating a litigation propensity score to determine the claims that may lead to litigation.

Are you getting the most from your insurance claims data? Feel free to share your thoughts.

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