Big data analytics helps organizations harness data and use it to create new business opportunities. In the same way, it has potential to offer a lot to insurers. For instance, with so many claims to handle, adjusters just do not have the time to sift through all the data to evaluate each claim. At the same time, they may not make the best decision if they miss a valuable piece of information. That means many of their decisions are based on experience, guess work and the limited info that is readily available on hand.
For this reason, big data analytics is playing an increasingly important role in the insurance business as it helps adjusters to inspect close, handle on priority basis and do much more.
Here are six ways how big data analytics can help insurance industry to make a big difference
Fraud detection- Most of the insurance claims in practical are said to be fake and fraudulent. So, big data helps in detecting fraudsters in time who often manipulate and get around the rules. Analytics uses a combination of rules, modeling, text mining, database searchers and exception reporting to identify fraud sooner and in a more effective way at each stage of the claims cycle.
Subrogation- Subrogation (Subro) is a process where an insurer pursues a third party that caused an insurance loss to the insured. This is done as a means of recovering the amount of the claim paid to the insured for the loss. When data volumes increase, the opportunity to make a claim from the third party decreases from police records, adjuster notes and medical records. Text analytics searches through this unstructured data to find phrases that typically indicate a subro case. By pinpointing subrogation opportunities earlier, you can maximize loss recovery while reducing loss expenses.
Settlement- In order to cut down costs and ensure transparency, insurers often implements fast-track processes that settle claims on instant note. But in most such cases, settling a claim will lead to overpay. Any insurer who has seen a rash of home payments in an area hit by natural disaster knows how that works. By analyzing claims and claim histories, you can optimize the limits for instant payouts. Analytics can also shorten claims cycle times for higher customer satisfaction and reduced labor costs. It also ensures significant savings on things such as rental cars for auto repair claims.
Loss reserve- Whenever an insured first reports a claim, it is almost impossible to predict its size and duration, even if the best data is available on hand. But accurate loss reserving and claims forecasting is essential, especially in long tail claims like liability and workers compensation. Analytics can more accurately calculate loss reserve by comparing a loss with similar claims. Then, whenever the claims data is updated, analytics can reassess the loss reserve, so you understand exactly how much money you need on hand to meet future claims.
Activity-It makes sense to put your more experienced adjusters on the most complex claims. But claims are usually assigned based on limited data – resulting in high reassignment rates that effect claim duration, settlement amounts and ultimately, the customer experience. Data mining techniques cluster and group loss characteristics to score, prioritize and assign claims to the most appropriate adjuster based on experience and loss type. In some cases, claims can even be automatically adjudicated and settled.
Litigation- In general, a major portion of an insurance company’s loss adjustment settlement ratio goes to defend disputed claims. Insurers can use big data analytics to calculate a litigation propensity score to determine which claims will move to the litigation channel. As soon as the data is available, the company can assign those claims to senior or most experienced adjusters who can settle the claims outside the court on an amicable settlement for a petite amount.
Finally, big data analytics can deliver a measurable ROI with cost savings.
So, want to embrace all the benefits of analytics on one go?
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