Sustainable underwriting: How insurers can account for ESG risks and enable dynamic pricing

By integrating more data from a more diverse range of sources and automating workflows, insurers can develop meaningful ESG scores for rating and pricing risk

In brief:

  • Traditional underwriting approaches are not sufficient to identify and manage the complex risks presented by climate change.
  • Tomorrow’s top performers will integrate AI-enabled tools and machine learning into their underwriting workflows to enable dynamic pricing and realize the benefits – including increased efficiency, improved risk selection and more profitable pricing.
  • Given the imperative for change, insurers can seize ESG as an opportunity to drive broader transformation in the underwriting function.

Environmental, social and governance (ESG) matters are having a major impact on the insurance industry, with profound effects being felt across the business, from new product development to investment strategies to brand positioning. ESG is shaping the C-suite agenda, too, with more insurers questioning whether they should provide coverage to coal plants, oil pipelines and other carbon-intensive businesses. These can be difficult decisions when certain customers can cause increased loss ratios due to climate-driven natural catastrophes, litigations related to social and governance issues.

Underwriters are very much on the front lines of the ESG revolution. It’s hard to overestimate the difficulty and complexity in assessing the wide range of risks from climate change. Those risks range from the physical damage caused by more frequent and severe storms to the disruptions caused by the transition to a greener economy. Property underwriters are focused on reducing their exposure to the physical impacts of climate change (e.g., coastal real estate threatened by hurricanes) and adjusting their pricing in line with these risks. Liability underwriters are carefully watching the growing number of ESG-related lawsuits and seeking forward-looking insights as to what sectors might face such litigations in the future.

But, according to recent research from Capgemini, relatively few insurers have begun factoring sustainability into their underwriting practices. Fewer than half of P&C insurers embed ESG scores in the underwriting process. Less than a third (30%) offer preferential conditions for customers that adopt sustainability initiatives and even fewer (27%) restrict access to “brown” or unsustainable companies.

Underwriters assessing price and risk in an ever-evolving landscape need a clear ESG underwriting policy and a repeatable approach that is standardized, transparent and automated. Realizing that vision requires insurers to incorporate richer data from more sources and deploy predictive models so underwriters can more effectively and accurately evaluate and price submissions. Ultimately, such an approach will enable dynamic pricing capabilities, which will be necessary to achieve underwriting excellence for the ESG era.

More broadly, more intelligent underwriting is just one way insurers can play a proactive, leadership role in helping to create a more sustainable economy and help society mitigate the biggest threats from climate change. It can also help the industry develop effective risk advisory and prevention solutions that all types and sizes of businesses need.

Continuing the modernization journey 

In one sense, ESG is another force driving the need for underwriting transformation. Insurers have long been working to automate core underwriting processes and integrate real-time and non-traditional data sets into their pricing and risk selection models. Similarly, they’ve been adopting the most advanced analytics tools in more sophisticated ways (e.g., predictive modeling, visualization). They were primarily motivated by the need to develop highly personalized, even individualized, products rather than the one-size-fits-nearly-all offerings of the past. The pressure to get to market faster was another factor.

The requirements of ESG have increased the urgency of the drive to automate, integrate and streamline. Personalization remains a priority, too; after all, different types of businesses in different regions will present distinctly different risk profiles.

And there’s no talking about underwriting transformation without talking about the need to break free of the constraints of outdated legacy systems, which continue to challenge many insurers looking to modernize their underwriting functions.

Moving from integrated data to ESG scoring

Outside transformational goals, underwriting leaders face practical questions relative to ESG – primarily, how to deliver useful data to underwriters so they can more effectively model new types of risks. New sources can include more detailed climate and weather information, real-time data from sensors connected to the Internet of Things (IoT), third-party liability databases, and even unstructured data from call center interaction or claims adjustment documents. Using artificial intelligence (AI) on these enhanced data sets will enable insurers to gain richer visibility into all of their different customers’ risk profiles, on climate-related and other types of risks.

Ideally, this transparent data can be fed directly into current environments (including the workbenches underwriters use every day) and embedded into existing workflows to generate ESG scores that evaluate customers’ risk exposures relative to ESG. Using AI-powered analytical tools to uncover new insights and machine learning to continually sharpen their models will allow insurers to produce ever more precise risk assessments and pricing. The end goal is to establish an ESG scoring process that is fully digitized for roll-out at scale and to model these complex risks and tailor pricing and risk selection processes to the full range of commercial customers.

Fundamentally, ESG scores measure a company’s exposure to long-term environmental, social, and governance risks. The score can indicate the probability of companies experiencing future losses from harmful events, assisting underwriters as they make decisions about account and portfolio pricing and profitability. ESG scores can also be used to target the right behavioral incentives (e.g., switching to EV fleets, installing solar panels) that can lead to premium discounts. All types of insurers need these capabilities, including specialty carriers and reinsurers, which could apply the principles of ESG scoring across their entire portfolios.

And ESG scores are useful not just for modeling the risks for oil and gas, automotive, construction, agriculture and other carbon-intensive sectors; insurers also need ESG scoring models that are easily applicable to average commercial customers.

We are not talking about the ESG scores being developed and released by many different rating agencies and investor groups during the last year or two. Rather, we mean a rating or underwriting toolset or framework that enables insurers to model ESG risks much more efficiently and effectively. Certainly, third-party ESG ratings might be a useful input; just one of many examples of new data that will enhance insurers’ overall underwriting approach.

Recent market developments point the way forward. Global insurers have developed underwriting processes and risk advisory services that identify risks requiring ESG assessment and provide tools for underwriters at the appropriate levels of authority. A large brokerage is partnering with a global carrier on a ESG risk rating measure.

In our view, these are starting points of a journey where ESG factors are embedded into underwriting processes, with more granular data continuously improving the accuracy of ESG scores. Underwriters with access to multiple scores and support from analytics team will be able to best assess and price risk.

The power of dynamic pricing

When scoring models are intuitive and work smoothly within existing workflows, they enable underwriters to evaluate risk and dynamically price exposures, which point toward the reality of real-time risk visibility. Dynamic pricing is particularly powerful relative to the complex risks associated with climate change. But it offers benefits of modeling any type of risk, making it a high-priority capability for insurers. Specifically, dynamic pricing allows insurers to responsively modify pricing and ratesas macroeconomic conditions change, competitive threats emerge and customer needs and preferences evolve. They can also improve risk selection through the use of more granular and timely data, including new variables such as changes in carbon emissions and new IoT data, that reflect individual behaviors and policyholder risks.

How to realize the vision

Insurers that are ready to advance their underwriting capabilities and move toward dynamic pricing should consider the following incremental steps.

Build the roadmap: Create a focused innovation team or engage external partners to develop the long-term underwriting vision, including real-time risk visibility, data-driven workflows, predictive analytics and no-touch processing.

Prep the tech: Design data storage and data ingestion capabilities along with APIs to integrate with core underwriting systems, including workbenches and pricing platforms, which allows insurers to take advantage of new data streams and will help boost adoption.

Prototype and pilot to test and learn: Focus on a specific product line, sector or geography for experimenting with new underwriting models; consider adding data sources incrementally to your underwriting workbench or specific workflows and generate insights accordingly.

In conclusion

Just as it has become a huge factor in banking and asset management, ESG will become more prevalent in insurance, both as a source of new risk and of strategic opportunity. Consumers and investors alike expect insurers to help facilitate the transition to a greener economy.

But a new underwriting approach is necessary for ESG largely because climate-related risks are unlike other risks insurers face. More data and more powerful analytics are necessary because climate-related threats must be modeled into the future, rather than being assessed primarily based on past events.