Insurance is a means of protection from financial loss. It is a form of risk management primarily used to hedge against the risk of a contingent, uncertain loss. Here are six ways where big data analytics can improve insurance claims processing:
One out of 10 insurance claims is fraudulent. Most fraud solutions on the market today are rules-based.It is to easy for fraudster to manipulate and get around the rules. Predictive analysis, uses a combinations of rules, modeling, text mining. Database searches and exception reporting to identify fraud sooner and more effectively at each stage of the claim cycle.
Opportunities for subro often get lost in the sheer volume of data most of it in the form of police records, adjuster notes and medical records. Text analytics searches through this unstructured data to find phrases that typically indicate subro case. By pinpointing subro opportunities earlier, you can maximize loss recovery while reducing loss expenses.
To lower costs and ensure fairness, insurers often implement fast-track processes that settle claims instantly. But settling a claim on the fly can be costly if you overpay. Any insurer who has seen a rash of home payment in an area hit by natural disaster knows how that works. By analyzing claim histories, you can optimize the
limits for instant payout. Analytics can also ensures significant savings on things such as rental cars for auto repair claims.
Loss reserve – When a claim is first reported, it is nearly impossible too predict its size and duration. 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 claim data is updated, analytics can reassess the loss reserve, so you understand exactly how much money you need on hand to meet future claims.
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.
A significant portion of a company’s loss adjustment expense ratio goes to defending disputed claims. Insurers can use analytics to calculate a litigation propensity score to determine which claims are more likely to result in litigation. You can then assign those claims to more senior adjusters who are more likely to be able to settle the claims
sooner and for lower amounts.
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