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How Data Science is Used in the Insurance Industry

In today’s highly-connected business world, Big Data is big business. Data is used across industries to gauge consumer demands, to improve operational performance, and to drive business growth. The insurance industry is no exception; with careful analysis, data has the potential to unlock new opportunities. This analysis requires a practice known as data science in insurance. In this guide, we will explore data science in insurance and illustrate the way this scientific method is transforming the insurance industry.

Big Data: The Foundation of Data Science in Insurance

In its past, the insurance industry was hampered by a lack of actionable data with which to reveal trends. The very nature of the insurance business remains somewhat unpredictable, but over the past 20 years, access to data streams has given insurance agencies a wealth of information. Big Data can come from a wide range of sources, including:

  • Mobile computing devices
  • Election statistics
  • Consumer purchasing habits and preferences
  • Satellite and Geographical Information Systems (GIS) platforms
  • Credit reports
  • Social media activity
  • Website traffic analytics
  • Government information sources

The introduction of cloud computing systems and powerful analytic tools has made the job of the insurance data scientist even easier, allowing for granular looks at a myriad of factors. With all this information readily available, insurance businesses gain significant competitive advantages.

The Transformative Effect of Data Science on the Insurance Industry

Harnessing data and using it to shape decision-making processes is important for any industry and business model. This is especially true in the global insurance sector. Data scientists carefully analyze a wide range of data sets by creating predictive models, which can then be applied to numerous insurance services like:

  • Fraud detection
  • Customer acquisition and retention processes
  • Personalized claims service
  • Personalized risk pricing
  • Identification of emerging insurance markets
  • Development of new insurance products and services

In simple terms, the better the insurance industry understands its own markets and its own consumer demands, the more efficient operations will be. Data makes that efficiency possible.

One of the transformative aspects of data science in insurance has been in the automotive and trucking insurance sector. The combination of telematics sensors (vehicle “black boxes” and GPS transceivers) and mobile computing devices like smartphones have facilitated claims processing. Eyewitness accounts of collisions are rarely reliable; data from telematics can more accurately pinpoint liabilities. These devices can also improve the speed with which claims are paid by reducing the amount of time needed for investigators to review crash details. Ultimately, the insurance consumer benefits, as telematics can show proof of safe driving practices, often resulting in lower premiums.

Technology in the form of smart home devices and wearables are also having an effect on the industry, particularly in the property and casualty sector. Internet-connected devices (the Internet of Things or IoT) like home security systems, smoke alarms, water sensors, and thermostats work to alert building owners of property-threatening conditions. In many cases, early detection can prevent an adverse event from happening, eliminating expensive insurance claims. As with the automotive insurance consumer, fewer claims often means lower premiums. For wearables, lifestyle data of users can be leveraged to streamline underwriting efforts and life insurance pricing, especially for those individuals in the profitable “healthy” demographic.

Operational Benefits of Data Science in the Insurance Sector

The insurance industry has come to rely on data to make critical business decisions. This data can uncover areas of inefficiency, waste, and fraud. The latter, fraud, costs the insurance sector millions of dollars each year. Prior to the use of data science in insurance, statistical models attempted to detect fraudulent activities. Now, data can more accurately predict and identify fraud, helping to manage the associated overhead losses.

Marketing of insurance is a time-honored practice to gain new customers. In the digital age, insurance marketing has taken on an entirely new set of practices thanks to data science. Digital marketers can target audiences based on specific demographic and geographic factors. With these information sources in hand, insurance marketing campaigns tend to be more cost-efficient while generating improved return on investment.

Finally, development of new insurance products and services is made possible by data science. Based on consumer preferences, demographics, and claims histories, forward-thinking insurance companies can tailor their options to meet specific consumer needs. The end result is a robust insurance market, filled with options for nearly every conceivable risk. Data is the way of the future, and in the coming years, the insurance industry will use data science to identify and capitalize on emerging markets.

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