Data Analytics in Insurance Claims Processing: Trends & Benefits

Whether it is boosting claim process efficiency or increasing consistency, claims organizations are turning to data analytics in insurance claim processing now more than ever to help them solve their most critical business challenges. A recent Deloitte study found that 56% of North American insurance industry leaders are currently exploring ways to increase their investment in data analytics. Using data analytics in insurance claims processing can create a variety of benefits if done right, including identifying trends before they become problems and helping claims organizations develop data-driven business strategies.

Data Analytics in Insurance: Three Trends

From building a solid foundation to setting up predictive modeling, there are a few key analytics trends that top-performing claims organizations are implementing across the industry today.

1. Data-Centric Strategy

To set up a successful data analytics strategy, it’s essential to get the foundation right and the foundation is one thing—data. Historically, the auto casualty and workers’ compensation industries have taken a report-centric approach when it comes to data analytics, with business users relying heavily on analysts to generate reports. Traditionally, in a report-centric system, business users submit reporting requests to an analyst based on a specific business question. Then data is extracted, transformed, and reports are generated and delivered to business users reactively—and often not in time to solve critical business problems. Today’s business environment—with an increased pace of change and more data available than ever—has highlighted the need for a more proactive, data-first approach that allows for quicker turnaround times and on-demand problem solving. A data-centric approach means your organization has made an investment in data as a strategic asset and has pre-defined and carefully curated key data elements that business users can utilize on-demand.

2. Self-Service, On-Demand Analysis

As mentioned above, a major issue with the traditional report-centric strategy is that it delays getting results due to analysts needing to conduct a discovery phase and locate and define data after a business user has submitted a report request. Pre-defining the data can help remove the need for data discovery and create an organizational standard, meaning any comparisons across organizations or lines of business will be fair and meaningful. This approach allows business users to not only leverage data on-demand to answer questions without an analyst but also to compare their results to other areas in the business and discover opportunities to improve financial results and gain operational efficiency. At the same time, organizations need to be careful not to fall into the trap of trying to “over standardize” the company’s business metrics. Instead, acknowledge areas where there are different definitions of related data and seek to define and document these differences.

3. Predictive Analytics

Once an organization has these foundations in place, business users can then begin to utilize predictive models to help them solve problems before they arise. In fact in a recent industry survey Mitchell conducted, industry professionals said they believed predictive analytics was one of the top technologies that would have the greatest influence on the workers’ compensation industry in the next five to 10 years. With a host of traditional analytics approaches and the recent developments in machine learning and artificial intelligence (ML/AI) to rely on, this enthusiasm is not surprising. In fact, with data science toolsets that are increasingly accessible to typical organizations, the future is indeed bright.

From automation to adjuster guidance, predictive analytics in insurance can have an infinite number of applications in the claims process. In that same Mitchell survey, respondents said they believe predictive analytics will play the biggest role in triage, severity and claim reserving in the future.

Benefits of Data Analytics in Insurance

Data analytics—especially programs that are set up using a data-centric, self-service strategy—can take the guess work out of claims processing and help companies understand business trends and challenges in real-time. By getting a clear picture of what’s going on in their operations through data analytics, claims organizations can make better, data-driven business decisions. For example, a company may be able to see that a certain provider is charging higher than its peers for comparable codes and decide to send bills from that provider to their special investigations unit, or they may identify that emergency room cost is up in a particular jurisdiction and implement specific workflow rules to better address those types of bills.

By using predictive analytics, organizations can send claims down the right path from the start by facilitating appropriate triage based on predicted claim severity or other factors, which in turn, can help boost automation and improve workflow efficiency and overall claim outcomes. For example, a predictive model could identify a low-dollar, low-severity claim and automatically push it through for straight-through processing, or it could identify that a claim has certain characteristics that put it at high-risk for complications and recommend case management or other tactics.

This type of data-driven decision making can help organizations on the way to improving their biggest challenges—whether that’s responding to cost pressures or improving workflow efficiency—and to catch other minor concerns before they become full-blown issues. Setting up a good foundation and data analytics program can also help organizations develop proactive strategies based on past success and industry-level comparisons. While analytics themselves won’t solve the challenges, they can help claims managers and leaders respond to critical challenges and come up with data-driven insights to help set their organizations up for success.