Six ways big data analytics can improve insurance claims processing |
A 1 percent improvement in the loss ratio for a $1 billion insurer is worth more than $7 million on the bottom line.
Claim Litigation Analytics (CLA)
Six Areas Where Analytics Can Make A Big Difference:
Fraud and Behaviorals– One out of 10 insurance claims is fraudulent. How do you spot those before a hefty payout is made? Most fraud solutions on the market today are rules-based. Unfortunately, it is too easy for fraudsters to manipulate and get around the rules. Predictive analysis, on the other hand, uses a combination of trending rules, behavior modeling, text mining, database searches and exception reporting to identify fraud and predictive verdict patterns sooner and more effectively at each stage of the claims cycle (as well as the key behavioral profiles of material parties based on predictive patterns of performance).
Subrogation – 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 a subro case. By pinpointing subro opportunities earlier, you can maximize loss recovery while reducing loss expenses.
Settlement – 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 payments in an area hit by natural disaster knows how that works. By analyzing claims and claim histories, and jurisdictional patterns you can optimize the limits for instant payouts. Analytics can also shorten claims cycle times for higher customer satisfaction and reduced labor costs by using a priority tradeoff matrix. It 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 to 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 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 – 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. Why make analytics a part of your claims processing? Because as insurance becomes a commodity, it becomes more important for carriers and brokers to differentiate themselves. Adding analytics to the claims life cycle can deliver a measurable ROI with cost savings. Just a 1 percent improvement in the loss ratio for a $1 billion insurer is worth more than $7 million on the bottom line.
Provide advice to clients on their property/casualty insurance programs before others.
Differentiate your company or brokerage against your close competitors.
This approach cost a dime on the dollar and results in tangible partnerships and profits
In closing, Major Market companies such as ACE, Lockton, Caltrans and Amazon invest in capitalized analytics to fuel growth, and safety.