The Future Of Insurance: Analytics, The Internet Of Things, And Machine Learning

Andy Hirst

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 insurers’ increasingly large ecosystems of partners and suppliers offer a wealth of structured and unstructured information that can be used to drive new business models, greater efficiency, and increased competitiveness.

This is also highlighted in a recent report, “Insurance Megavendors Shift Focus to Digital Platforms,” where Gartner compared some of the top vendors in the industry on their data and analytics capabilities.

Digital boardroom

Gartner underscored the importance of taking advantage of a digital boardroom, which includes access to a public cloud. Prepopulated 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.

In-memory analytic capabilities and an intuitive interface enables users to identify problems and 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

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 better understand customers, situations, and the environment.

For example, advanced analytics will allow customer sentiment about products and brands to be analyzed, enabling 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 policyholders are driving, such as the speed they travel, the amount of sharp braking, how quickly they take corners, the time when they drive, and a whole host of other factors. With 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. This insurance and reinsurance broker is dedicated to weather risk management. It has created an app that lets customers select their policy specifications, including geolocation, coverage period, and weather parameters. The company then uses an in-memory computing 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 are gaining fresh momentum as technologies that can radically change how the insurance business is conducted. In Accenture surveys, 82% of insurance executives reported they were investing more in embedded AI solutions to improve their business processes, and 27% expected AI to completely transform their organization over the next three years.

Embedding machine learning intelligence into a cloud platform and applications supports 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 in order to identify customers that are about to churn and take proactive action to keep them.

Another area where machine learning can be used is claims leakage. By taking observations and findings from claims audits, machine learning can predict which claims have a high probability of resulting in 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.

A further example is 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. As a result, they can help insurers eliminate false positives, quickly spot potentially fraudulent activity, and take action to avert fraud.

An opportunity to accelerate processes and decision making

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

Learn how to bring new technologies and services together to power digital transformation by downloading “The Future Services Sector: Connected Services for Continuous Delivery.”

Andy Hirst

About Andy Hirst

Andy Hirst is vice president of Banking Solutions, SAP Banking Industry Business Unit, at SAP. He is responsible for driving the success of the SAP go-to-market strategy in Line of Business Cloud Applications and Analytics in Financial Services. Previously, Andy was responsible for Capital Markets solutions for banking. Andy is an expert in Big Data and analytics use cases in financial services and has been involved in many digital banking initiatives for banks.