How data and analytics techniques are changing the way insurers approach the claims process.

In my previous post, I explored the advances in technology and the opportunities they present the claims organization. This week let’s look at the data deluge resulting from new technologies and how insurers can use analytics to get ahead.

In business generally, it is rapidly becoming apparent that the next competitive battleground will be companies’ relative ability to turn the vast quantities of data they generate into actionable insights. To close the loop between what happened in the past and what should happen in the future, companies are investing in sophisticated analytics capabilities.

Claims organizations that have inflexible and heterogeneous IT systems, face considerable challenges when it comes to:

  • Consolidating data.
  • Determining what can be used for advanced analytics.
  • Formulating plans to put that data to work to drive improved claims accuracy and efficiency.

To develop an analytics capability, insurers need to identify and implement a technology platform to support advanced analytics. For insurers running different claims platforms for various parts of their businesses, it’s a daunting technical challenge to determine which analytics tools can plug-and-play with existing applications and infrastructure portfolios, as well as to acquire the analytics talent necessary to drive results.

But relatively modest investments can lead to increases in efficiency and a reduction in overall costs for claims organizations that use data to digitize and/or automate existing processes, or to make underwriting decisions, price risks, predict losses and manage claims payouts.

Predictive analytics can be a particularly effective tool for:

  • Improving claims routing, by allowing organizations to route claims to the most appropriate adjuster as well as to proactively identify low- and high-risk claims and ensure they are dealt with as expeditiously and efficiently as possible.
  • Mitigating high-risk claims that might initially appear low-risk. Predictive analytics can help insurers identify the common characteristics of claims that have appeared to be low-risk but have turned out not to be. Carriers can then apply those characteristics to a new analytical model and scan existing claims for warning signs.

Claims analytics can also be used to enable automated claim handling, whether that’s straight-through processing, in which a claims process is fully automated, or the linking of several automated processes, which has the potential to enable end-to-end, no-touch claim handling.

Taken a step further, claims analytics can be extended to artificial intelligence, where machines no longer just execute algorithms and tasks, but learn on the job and take new actions based on what they have learned. Artificial intelligence has the ability to manage far more data than a person or single predictive model can reasonably handle, and consequently, can find patterns in data that people might miss or not be able to see.

The data itself is another important consideration. Traditionally, insurers have relied on their own data, and upon structured information from various bureaus and agencies to make decisions. But in the new digital world, data from external sources—such as digitized government and third-party databases, social media comments, product reviews and discussions, and devices connected to the Internet of Things—offers further opportunities to gain insights that lead to growth and improve the loss ratio.

Data and analytics techniques are creating opportunities that have not existed before, enabling claims organizations to make better and faster decisions, and changing the way they approach the claims process.

In my next post, I’ll look at the workforce needed to thrive in this new digital environment.

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