Data, analytics, and technology have become table stakes in the global insurance industry. Without that essential trio, companies will find it difficult to differentiate themselves or even remain competitive in areas like product, pricing, risk management, claims adjusting, and client services. These are all areas that are enhanced with effective data, analytics, and technology.
Insurance organizations have long understood and engaged in data collection; it is a fundamental aspect of many industry processes, and the sophistication of how firms use data they collect has increased over time. What’s evolved more significantly in recent years, according to Janet Wesner (pictured below), head of analytics at Munich Re US, is the emergence of third-party data providers that can enrich the internal data collected by organizations, and the development of technology, analytics, and modeling tools that can enhance decision-making.
Tim Brockett (pictured above), EVP and head of specialty, Munich Re US, explained: “In the insurance industry, we don’t know our costs in advance, so we need to make the best possible use of quality data and predictive models to predict our future costs. There’s an opportunity now to put really good data-driven tools in place that can enhance our pricing, our underwriting, and our analytics around claims and customer segmentation to improve our profitability, and understand our risk.”
Lots of insurance companies kick off their analytics journey in pricing segmentation. This is especially true for personal lines carriers who have access to end-consumer data containing many characteristics that can be directly correlated to risk, which can then help to price policies (e.g. personal auto) more accurately. The recent explosion of third-party data, lots of which have been generated through the increase in online and digital activity, can also help insurers with marketing.
These tools are also helping insurers to become more efficient in their claims operations. Wesner commented: “Oftentimes, the claims department is strapped for resources, but analytics can help insurers identify where to allocate and prioritize resources for claims management and so on. If we can use technology to determine – the complex claims that require attention from a senior claims adjuster, and the more standard claims – we believe that can bring huge benefits for insurers.”
In recent years, there’s also been a significant uptick in the use of weather and geospatial data analytics, Brockett added, particularly by insurers with catastrophe-exposed property business. He noted: “There’s a treasure trove of data available that can help inform your view of risk and help insurers make the best possible pricing and risk management decisions.” Geospatial capabilities can also help insurers with near real-time portfolio visualization and optimization.
Before jumping into data analytics, there are certain best practices that insurance organizations should strive for. First and foremost, it’s important to have “strategic focus” for how and where data analytics will be applied. Wesner commented: “Companies often try to apply analytics to everything, but having a strategic focus is really important. There are so many business problems that can use analytics, so unless you have that strategic focus, analytics almost becomes a utility that doesn’t have any regard to the value or significance of the problem you want to solve. This is especially important for companies that are earlier on their data analytics journey. As you conquer each problem [or tap into each opportunity], you can expand your area of focus.”
The next important piece is data quality and ensuring that the data collected can help an organization meet its strategic goals. In the past, this has sometimes been overlooked, as Wesner noted: “It was often the case that the technology and modeling piece had the glamor, but today, the most sophisticated technology and modeling approaches tend to be cloud-based or open source, which means that internal data assets and the human organizational processes are now the truly differentiating aspects of analytics.”
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It’s important for insurers to remember that analytics is just one part of a far bigger process, and overall strategic success requires engagement from multiple stakeholders across the organization, including underwriters, actuaries, claim adjusters, and of course, the data and technology experts.
“You may have identified certain data or risk characteristics that might be predictive, but that doesn’t necessarily mean it can be used in pricing,” Brockett pointed out. “First of all, you need a significant amount of that data to make it a credible insight, and then you’ve got 50 states with potentially 50 different opinions on whether it’s appropriate to be used in a rating scheme. So, there’s still a way to go in terms of what all of this can mean to an end-consumer. We also need to bring in the underwriting teams and other experts around the company to really validate our predictive models and determine if the results are meaningful.”
Munich Re partners with clients throughout their analytics journeys to ensure they’re following these best practices and getting the most out of their data strategy. The reinsurer works with clients on customized analytics projects, while also providing consulting services, technology platforms, and more specific catastrophic modeling capabilities.
“We don’t want to go with a one-size-fits-all approach. We want it to be more tailored to the client, really understand what they need, and then develop our services to meet their needs,” said Wesner. “We really see our clients as partners; we’re in the same boat. We want them to be successful, because in the long-run their success is our success. Our analytic strengths may not be in the same areas, so to the degree that we can complement and make each other better, that’s the ideal situation.”
Learn more about Munich Re’s client-centered and solution-oriented approach to help you stay competitive at https://www.munichre.com/us-non-life/en/solutions/reinsurance.html