Why insurers need to rescue underwriters from siloed data

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In 2008, Accenture published the results of the first P&C underwriting study in partnership with Institutes. This report, which is the longest-running longitudinal study of underwriting in the insurance industry, provides a holistic picture of where underwriting has been and where we are going. Namely, it shows us the relationship between the goals set by leaders over the past decade and the tangible progress made as a result of these initiatives.

One of the key takeaways that I have drawn from 2021 P&C Underwriting Survey is that over the past 15 years, the position of underwriters has not improved much. Despite technological breakthroughs, underwriters continue to face the same challenges as they did in 2008, and in some areas underwriting’s status as a core function of the insurance business has deteriorated.

In my previous posts, I have discussed the transition to automation, the impact of technology on the underwriting process, and the declining focus on underwriters. In this post, I want to emphasize the importance of an underwriter’s skill set and explore a different approach to combining technology with this skill set that will make the job of underwriters easier and more efficient.

Back in 2008, our survey showed that over 40% of underwriters’ time was spent on non-core tasks. Underwriters struggled to abandon outdated systems and implement new solutions. Fast forward to 2021 and the latest survey shows that only 35% of underwriters believe technology has reduced their workload. In 2008, this figure was almost equivalent – 36%.

In both 2008 and 2021, the lack of data integration was cited as an emerging technology issue, with 72% of respondents in both years reporting this issue. In 2021, 79% of respondents reported that a lack of process integration was the top reason technology was negatively impacting their workload.

This data made me think about the day to day responsibilities of an underwriter and wonder why technology hasn’t made the underwriting process easier. Today’s responses show that the underwriters themselves are paying less attention. There is empirical evidence for this, including data showing that respondents generally perceive recruitment, training, and retention programs in their organizations as insufficient.

In addition, attention to the main controls and underwriting discipline is reduced: only 30% of the underwriter’s time is spent on risk analysis and quotation. Risk analysis is the main competence of the underwriter. Their job is to analyze data from different sources and summarize them to make an accurate (and profitable) decision. From this point of view, I see the underwriter as the researcher of the underlying data.

The prestige and value of the underwriting profession has plummeted over the past 15 years, leaving underwriters with the same problems they faced over a decade ago. Insurers are prioritizing cost minimization and “demystifying” underwriting by automating the process or reducing the underwriter’s role in risk assessment.

We did this by shifting the work to underwriters, providing new risk and pricing models to help guide decision making, and trying to use automation to make underwriting easier. None of these initiatives is negative in and of itself. All are well-suited for assessing simpler, homogeneous risks while reducing costs and improving pricing consistency. But they miss the fundamental issue of more complex underwriting.

The real problem is that underwriting is still a paper process, with important data stored in PDFs and spreadsheets attached to emails from brokers. To assess risk, underwriters still have to navigate through various documents looking for data formatted differently depending on the broker it comes from.

Although we tried to simplify the underwriting processes, we did not focus on improving the data science aspect of underwriting. This requires more data availability. We need to implement solutions to help underwriters extract, manage and evaluate All their data in one place in a way that also provides relevant context and deeper understanding.

Many organizations have taken important steps to become data driven over the past 15 years. Insurance has always depended on data, but it’s time to rethink how data aggregation and analysis are optimized in underwriting processes. If insurers want to see more efficiency, consistency and quality in their risk and pricing decisions, we can’t focus on leaving that job to the underwriter. We need to help underwriters best analyze information, identify patterns, and make decisions based on a holistic view of the applicant.

To do this, we need to consider third generation underwriting platforms like the ones I discussed in my previous post. It really comes down to five simple priorities:

Invest in solutions that extract all the data underwriters need from their data warehouses, gathering information from PDFs and nested spreadsheets in one place, ultimately eliminating this method of communication entirely.
Organize the information, knowledge, and data involved in making critical underwriting decisions: triage, risk assessment, and pricing.
Present information in context. For example, allow underwriters to compare new applications with similar applications to help them understand how an application or renewal is different.
Integrate this data-driven, analytics-driven approach into your existing workflows to make things easier.
Set up quality controls, measures, and feedback mechanisms to improve underwriting quality and consistency within the new process.

Fortunately, we are already seeing insurers taking steps to improve in this area. The 2021 survey shows that 67% of insurers will prioritize investment in underwriting platforms over the next three years. 71% want to add predictive analytics to their tech stack, and 66% plan to invest in customer and broker portals, another way to streamline data collection.

If you’d like to learn more about how we help companies realize these five ideas, let me know.

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