6 ways big data analytics can improve insurance claims data processing

What does big data have to do with insurance claims management? A lot, as it turns out. Insurers sift and search and sort incredible amounts of data – adjusters’ hand-written notes, data from fraud lists, and information from claims management systems along with claims databases, such as the National Insurance Crime Bureau (NICB) claims database in the US.

Are you getting the most from all that insurance claims data?

With so many claims to handle, your adjusters don’t have time to sift through all the data and carefully evaluate each claim. Unfortunately, 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, gut feeling and the limited information that is readily at hand.

For this reason – and many others – big data analytics plays an increasingly important role in insurance claims management. Working alongside adjusters, analytics can flag claims for closer inspection, priority handling and more.

Even fractional improvements in the combined ratio on a $1 billion book of business can result in millions of dollars in compounding returns.

Here are six areas where analytics can make a big difference with insurance claims data:

Fraud Forbes has stated that an estimated 20% of insurance claims are fraudulent. How do you spot those claims before a hefty payout is made? Most fraud solutions on the market today are rules-based. Unfortunately, it's too easy for fraudsters to manipulate and get around the rules. Predictive analytics, on the other hand, uses a combination of rules, modeling, text mining, database searches and exception reporting to identify fraud sooner and more effectively at each stage of the claims cycle.

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 – all forms of big data in healthcare. 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, 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 – 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 insurance 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 affect 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 insurance claims data processing? Because as insurance becomes a commodity, it becomes more important for carriers to differentiate themselves. Adding analytics and AI 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.

Related content: 3 ways generative AI can level the field with health care fraudsters

Instead of reinventing the wheel, why not use generative AI to enhance the efficiency of special investigative units and payment integrity teams? Consider three examples of how these teams can use generative AI to fight back against fraudulent claims (and more). Learn how:

  • A digital assistant can help with claims data.
  • Synthetically generated data can be used to train fraud models.
  • GenAI can serve as a case management assistant. 

Read more