Data analytics: An opportunity to rethink risk and insurance?

Businesses and their insurers constantly have to keep pace with fast-changing and complex risks, such as extreme weather events to inflationary pressures or cyber threats.

Fortunately, data and analytics increasingly helps us better understand risk, and supports more informed decision making. Equipped with more accurate and timely information, insurers are able to engage in more meaningful conversations with their customers, as well as offer products and services that go beyond traditional risk transfer and advice.

Most insurers, to varying degrees, have invested in data analytics, using internally generated data – gathered from their own operations, customers and brokers - to inform underwriting, claims and risk engineering. Increasingly, however, insurers have access to a growing bank of external risk data, from public sources and specialist third parties.

Delivering timely insights

In a world of fast-changing and complex risks, insurers need to obtain an as-accurate-as-possible view of their customers and their risks. To this end, data analytics opens up multiple opportunities to improve the effectiveness and efficiency of underwriting, claims and risk engineering, as well as unlock and deliver key risk insights.

Enterprise-wide data analytics can help insurers collect relevant data and deliver it to key decision makers when it is most needed. Algorithms can support risk professionals at key points in the underwriting, claims or risk engineering process, providing advice and guidance, alerting them to areas of concern, or prompting them to ask critical questions, which could be overlooked during busy renewal periods.

Underwriters are also able to draw on third-party data, enhanced by their own data and knowledge, to support underwriting decisions and help fill gaps in information. Insurers are increasingly collaborating with technology companies to access data on a wide range of risks, such as climate change, cyber security, litigation, supply chains and political risks, as well as satellite imagery or data generated by sensors or tracking devices.

Birds eye view

In addition to supporting day-to-day processes and decision making, data analytics also comes into play at portfolio level, enabling a more dynamic and proactive approach to understanding risk. AI-enabled portfolio analysis helps identify emerging trends early, prompting further investigation and informing underwriting and risk management strategies. It can also set in motion measures to improve underlying risks and address insurability, working with customers to take risk management actions that prevent and/or mitigate losses before they become a problem.

Data analytics also allows risk professionals to be more creative and explore different risk perspectives. It provides underwriters and risk managers with tools to explore risk data, to test out hypothesis or seek answers to specific questions. For example, an underwriter can instantaneously dig deeper into a risk and examine if recent loss activity or a certain exposure is limited to a location or customer, or is in fact a trend in the wider portfolio.

Creating new products and services

Insurers like Zurich are continually expanding their value proposition, looking to address an ever-wider range of risk-related needs. Zurich Resilience Solutions was launched in 2021 to provide corporate customers with risk advisory services and the technology to manage complex risks. Data analytics are a key component of the Zurich Resilience proposition, providing customers with data and tools to identify and quantify risks, including as those related to climate change, supply chain and cyber.

In addition to offering customers with data, insights and advice from insurers’ experience and knowledge of insurable risks, data analytics presents opportunities to provide products and services that go beyond the industry’s traditional risk advisory capabilities. Insurers will increasingly be able to offer solutions that help companies address come of their wider business needs, such as environmental social and governance (ESG) reporting or supporting their transition to net zero.

Right data, right time

Creating a successful enterprise-wide data analytics capability is no easy task. For insurers, the challenge is to deliver data analytics at scale, to the right people, and at the right time. They need to build systems that combine internal and external data, and then use it to focus on the needs of customers and the use-cases that bring the most value.

To do this successfully, insurers need to integrate data analytics into day-to-day decision making and processes. It is critical that data analytics teams work closely with corporate functions, building trust and understanding. By embedding it into the business, the business units appreciate the capabilities and limitations of data analytics, while the analytics teams better understand and fulfill the needs of the business units.

Insurers also face challenges with the quality and reliability of data. Underwriters at times deal with incomplete risk and customer information. Such data gaps need to be supplemented with underwriters’ expertise and experience – the science and art of underwriting. As in real life, incomplete or imperfect data is likely to be better than no data. As long as the limitations and compromises are understood. There are also situations in which insurers cannot make compromises when it comes to data completeness and quality. For such instances in particular, it is important for insurers to ensure robust data governance within their organizations.

Competitive differentiator

The use and adoption of the latest data analytics methods and tools will continue to evolve in insurance, but the next few years are likely to see the capabilities of insurers reach the next level of maturity. In Zurich, for example data analytics is an integral part of how we conduct business, and we embed and expand its application more and more within decision-making process. We have invested in collaboration with leading tech companies and start-ups and recruited technology specialists and leading experts.

Data analytics represents a huge opportunity, and one that already creates competitive advantages for insurers that do it well. When supported by data analytics, insurers can engage in more tailored and informed conversations with their corporate customers, as well as offer a better product and service, more accurately priced and efficiently delivered.

Originally published on Commercial Risk Online on April 26, 2022.