In our last post, we discussed low-complexity underwriting. In contrast, high-complexity underwriting, such as workers compensation and global property and liability policies, requires a deep understanding of risk factors and a nuanced approach to pricing. The administrative work involved in these processes can be overwhelming, taking time away from the craft of underwriting. With the right data insights and automation, however, underwriters can be freed up to focus on the art and science of their work, providing more value to the organization and the clients they serve.
The Challenges of Underwriting Complex Risks
As an example of high-complexity P&C underwriting, let’s consider a workers’ compensation and property proposal for a national automotive manufacturer with many different locations, each designated for doing different, varied tasks:
- Underwriters would need to understand the manufacturer’s business operations, the specific nature of work performed at each site, the risks inherent in each task, and the measures taken to mitigate those risks.
- Underwriters also need to evaluate the manufacturer’s safety protocols, the history of claims and losses, and the potential for future risks.
- In addition to workers’ compensation, underwriters also need to evaluate the property risks, such as potential property damage from accidents, fires, or natural disasters.
- These only account for a small portion of the total data points that need to be considered, making it easy to imagine how challenging it is to juggle all the information.
As another example, consider a global general liability, property, and directors and officers liability proposal for a sports equipment retailer:
- Underwriters would need to analyze the retailer’s global operations, the nature of the products, the markets served, and the regulatory environment in different countries.
- Underwriters would also need to evaluate the risks associated with product liability, including the potential for injury or harm caused by faulty or defective products.
- Underwriters would then need to assess the property risks, such as potential damage to retail stores, warehouses, or distribution centers.
- Underwriters would additionally need to evaluate the risks associated with the directors and officers policy, including potential litigation related to corporate governance or fiduciary responsibilities.
- Again, these are just some of the more immediate data points. Fold in other elements like crime, ESG factors, geolocation, and more and the sheer volume of data to process becomes hard to fathom.
Managing large amounts of information and making accurate risk assessments in this kind of underwriting is challenging, given the volume and complexity of the data involved. Underwriters need to gather content from various sources, such as company financial statements, industry reports, loss histories, and regulatory filings. They need to analyze the data using statistical models, machine learning algorithms, and other analytical tools to identify patterns and trends, then layer on top of those predictions an element of their own judgment and personal experience. They also need to assess the quality and reliability of data, given the potential for errors, omissions, or bias.
Moreover, these Underwriters must focus on these complex risk assessments that require detailed analysis and expert judgment while also ensuring they manage their throughput adequately. This is notable, because they often spend a significant amount of time — 30%-40% according to McKinsey — on administrative tasks such as data entry, data validation, and report generation. These tasks limit the Underwriter’s ability to provide adequate focus on the complex risk assessments and can lead to burnout, lower job satisfaction, and — ultimately — Underwriter turnover. That turnover, coupled with the slower response and turnaround times caused by the hyper-manual process, can in turn result in lower win and retention rates on otherwise desirable risks.
That’s why the automation of administrative tasks and the use of advanced analytics tools can be invaluable — freeing Underwriters to spend more time on complex risk assessments and value-add activities that leverage their expertise and experience.
How Rich Insights and Task Automation Can Enable More Effective Underwriting for Complex Risks
Insights and automation can combine to play a crucial role in improving the efficiency and accuracy of complex underwriting. By leveraging advanced analytics tools and machine learning algorithms, underwriters can streamline administrative tasks and focus on more complex risk assessments. For instance, automating data entry and validation can help underwriters save time and reduce errors. At the same time, machine learning algorithms can help identify patterns and predict risks with greater accuracy, providing rich insights to inform decisions.
Some other examples:
- Risk selection tools can automatically flag high-fraud-risk submissions for further review.
- Predictive analytics can identify emerging risks.
- Pricing algorithms that use machine learning can generate pricing recommendations.
By combining these tools with human expertise, underwriters can make better-informed decisions and offer more competitive rates without sacrificing profitability, ultimately improving the customer experience. Additionally, increased accuracy and efficiency help underwriters reduce the risk of mistakes and improve the underwriter’s (and ultimately, their broker’s AND customer’s) experience. Automation also helps underwriters stay on top of deadlines, flagging high-priority submissions and guiding next steps. Finally, insights gleaned from analytics can help underwriters make better decisions by identifying trends and patterns in data that may not be immediately apparent, driving more profitable risk selection and pricing.
The Importance of Skilled Underwriters in Complex Underwriting
However, automation can’t stand alone in driving the selection and pricing of these complex risks. Despite the increasing role of automation and analytics, skilled underwriters remain a critical component of proper underwriting.
Underwriting complex situations requires a deep understanding of the industry, the specific exposures involved, and the unique needs of each customer. It also requires a sense of craft or artistic skill, honed through experience, that helps underwriters make nuanced decisions and identify factors that may not be immediately apparent.
Therefore, recruiting and training skilled underwriters is essential for success in high-complexity underwriting. Underwriters must be able to balance the need for accuracy with the need for speed, working quickly without sacrificing quality. They must also be able to work collaboratively with colleagues and customers, communicating complex ideas in an easy-to-understand way. This can be developed with proper mentorship and training, but it relies on bringing in the right talent at the start. In the current market, this can be challenging: workforces are shrinking and 55% of insurance executives state that they see talent as a key growth limiter.
This makes retention of skilled underwriters equally – if not more – critical. The role of the underwriter is changing, and the most successful underwriters will be those who can adapt to new technologies and incorporate them into their decision-making processes. Companies that invest in their underwriters, providing ongoing training and development opportunities, will be better positioned to compete in the increasingly complex and dynamic world of high-complexity risk underwriting. By valuing the craft of underwriting and supporting the development of skilled underwriters, companies can build a sustainable advantage in this space.
Incentivizing Profitable Underwriting in Complex Environments
Effective underwriting is critical to the success of insurance organizations, especially in complex underwriting. However, underwriters are often incentivized to prioritize short-term premiums over long-term profitability, leading to conflicts with the craft of underwriting. To address this, organizations need to establish incentives that prioritize high-quality underwriting and long-term profitability in addition to short-term cashflow needs.
One way to incentivize profitable underwriting is to weigh expected profitability over immediate premium volume. For instance, organizations can reward underwriters for meeting profitability targets instead of just premium targets. This incentivizes underwriters to focus on selecting and pricing risks that will generate profitable long-term results instead of just short-term revenue.
To do this, organizations need to provide underwriters with the right insights and automation tools that enable them to make more accurate risk assessments and pricing decisions, including loss forecasting. This requires effective communication between business and technology partners to ensure that the technology is aligned with the organization’s desired outcomes. As previously mentioned, organizations should also invest in training, recruiting, and retaining skilled underwriters who can make the most of these tools and insights.
It’s important to recognize that people perform to their incentives. Incentivizing high-quality underwriting is critical to the success of complex risk analysis, selection, and pricing. By prioritizing long-term profitability, providing the right tools and insights, and investing in skilled underwriters, organizations can create a culture that values high-quality underwriting and enables underwriters to focus on the craft of their work.
Innovating = Empowering Your Most Skilled Underwriters
As we’ve noted, a proper understanding of underwriting complex risks often requires careful, painstaking analysis of a large amount of data. Recruiting and training skilled underwriters is essential for an insurance organization’s success. But as we have discussed in this article, organizations need to continue innovating in order to empower their underwriters:
- The administrative work involved in assessing complex risks can be overwhelming, taking time away from the craft of underwriting and burning Underwriters out.
- Leveraging insights and automation can streamline administrative tasks and free up Underwriters to focus on the art and science of their work, providing more value to the organization and the clients they serve.
Equipping skilled underwriters with advanced analytics tools, when appropriately incentivized, can help them make better-informed decisions, ultimately improving the customer experience and closing profitability gaps.