Data Analytics: How It Came About To Transform The Insurance Sector

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Several years of accelerating investment in data and data analytics are transforming the insurance industry.

To be accurate of course, data analysis is one of the historical pillars of insurance. Actuaries have used mathematical models to predict property loss and damage for centuries. When they sell policies, insurers collect large data-sets about their customers that are updated when those customers make a claim.

In recent years, as insurers have sought to become more relevant to their customers and more efficient, they have realized the strategic importance of their data investments. They want to harness data analytics to improve customer experience significantly, whilst cutting claims handling time and costs, and eliminating fraud.

Leveraging advanced analytics, and then integrating those results into their business processes needs to be an integral part of every insurer’s strategy.

Manually spotting troublesome claims early is challenging; working out strategies to mitigate the risk once identified is tougher still. The information needs to be delivered in a timely fashion (preferably instantaneously), into the natural workflow of the adjuster, possibly with a notification to the supervisor or large loss unit. The information delivered needs to not just raise an alert, but to explain the attributes which support the risk level, and propose a solution or work plan for the adjuster. This process should repeat itself in real-time as underlying data changes are made to the claim file, particularly for long-tail lines such as bodily injury.

Insurers are also turning to external data sources and adding more information about the claimant or injured parties, such as identity verification or social media data. However, there are limits and barriers to just adding external data points.

Putting machine learning into how data is collected and analyzed will help considerably in how insurers become more data-led and driven businesses.

For well-understood risks, old assumptions may no longer apply. Indeed, the future may not be like the past. For example, changes in technology (semi-autonomous vehicles) and human behavior (distracted driving) have already affected losses and their resulting claims in the familiar, well-studied area of personal car insurance.

And how insurers will work with data in new ways is by embracing new models of technology, like Internet-scale “data listening” that aggregates, cleanses, and updates petabytes of data to build risk models in.

Ultimately, the industry is moving towards applying machine learning, natural language processing, and other modeling techniques to their core and third-party data in support of both operational and risk analytics. This ranges from underwriting tools for evaluating and pricing risk through the scores used to make better operational decisions in service and claims after those risks become insured.

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