Data and analytics are—and always have been—at the very heart of the insurance industry. Successfully setting insurance premiums depends on being able to accurately analyze the risks involved.

And as digitalization takes hold, data and analytics are becoming even more important to the industry. In addition to the plethora of data that insurers hold in their own systems, the Internet of Things, social media, and the increasingly large ecosystems of partners and suppliers that insurers are building offer a wealth of structured and unstructured information that can be used to drive new business models, greater efficiency, and increased competitiveness.

SAP has worked with the insurance industry for many years, so we’re delighted with our score in a recent Gartner report—Insurance Megavendors Shift Focus to Digital Platforms.

Gartner scored vendors on a 0 to 4 scale, in which 0 is no capability and 4 is high capability. Source: Gartner (January 2017)

SAP Digital Boardroom

In the report, Gartner stated “SAP has launched the Digital Boardroom product, which includes public cloud to help companies.” Pre-populated with key performance indicators for the insurance industry—such as loss ratios and revenue—this technology provides executives and managers with the ability to analyze real-time data from all lines of business and operations, as well as external sources.

Using the in-memory analytic capabilities of the SAP HANA platform and an intuitive interface, users can identify problems and drill-down to determine their root causes. At the same time, the technology can be used to run “what-if” scenarios to test out possible future courses of action.

Internet of Things

In the report, Gartner also stated “While none of the vendors are far along with their IoT support, SAP has the most mature IoT vision.”

As sensors become commonplace and widespread in the home, workplace, and society as a whole, insurers will have the opportunity to use the structured and unstructured data they provide to understand customers, situations, and the environment better.

For example, advanced analytics will allow customer sentiment about products and brands to be analyzed, allowing insurers to adapt existing products. In addition, by combining social media data with information from IoT devices—fitness monitors for example—insurers can look for trends and opportunities to provide new products.

Another great example is how telematics is changing car insurance. By fitting a “black box” in cars, insurers can obtain real-time information about how policy holders are driving, such as the speed they travel, the amount of sharp braking, how quickly they take corners, the time at which they drive, and a whole host of other factors. Using this information, they can reward safer drivers by lowering their monthly premiums and penalize bad driving with additional costs.

They can also provide frequent feedback and advice via the Internet or mobile apps, helping to modify driving behavior. As a result, drivers can reduce their premiums and insurers can reduce risk, a win-win situation.

A further example is Meteo Protect—an SAP customer. This insurance and reinsurance broker is dedicated to weather risk management and created an app that lets customers select their policy specifications, including geo-location, coverage period, and weather parameters. The company then uses the SAP HANA platform to aggregate weather-related data, analyze risks, and price and underwrite the policy—all in real time.

Machine Learning

Machine learning and artificial intelligence (AI) aren’t new, but they’re gaining fresh momentum as technologies that can radically change how the insurance business is conducted. According to Accenture surveys, 82 percent of insurance executives reported more investments in embedded AI solutions to improve their business processes, and 27 percent expected AI to completely transform their organization over the next three years.

At SAP, we’re embedding machine learning intelligence into our cloud platform and applications to support more intelligent business processes. For example, machine learning can be used:

  • To collect dynamic data from a wide variety of channels—including customer interactions, policy claims, and payment information. This can then be used to look for critical events and indicators to identify customers that are about to churn and take proactive action to keep them.
  • In claims leakage. Using observations and findings from claims audits, machine learning can predict which claims have a high probability of leakage. These claims can then be treated with a greater level of care or handled by a higher-skilled claims adjuster, while other claims can be automated to settle them more quickly.
  • In fraud management. Machine learning systems can quickly recognize anomalies and patterns that are outside the norm, enabling them to separate the signal from the noise. Consequently, they can help insurers eliminate false positives, quickly spot potentially fraudulent activity, and take action to avert it.

The bottom line is that analytics and machine learning can process vast volumes of complex data faster and more accurately than humans. In turn, insurers can accelerate processes and decision making; adapt faster to changing markets, situations and requirements; and gain deeper insights into their customers, business, and the ecosystem in which they operate.

Read Insurance Megavendors Shift Focus to Digital Platforms

This article originally appeared in D!gitalist Magazine by SAP and has been republished with permission.