Why insurers need to rescue underwriters from siloed data | Insurance Blog

In 2008, Accenture published the results of the first P&C Underwriting Survey in partnership with The Institutes. As the longest-running longitudinal underwriting survey in the insurance industry, this report reveals a holistic picture of where underwriting has been—and where we’re going. Namely, it shows us the relationship between the goals leaders set over the last decade and what the tangible progress has resulted from those initiatives.

One of the key insights I gleaned from the 2021 P&C Underwriting Survey is that not much has improved for underwriters over the last 15 years. Despite leaps forward in technology, underwriters still face the same challenges they did in 2008 and, in some areas, the state of underwriting as a core function of the insurance business has worsened.

In my previous posts, I discussed the shift to automation, the effects of technology in the underwriting process, and the diminishing focus on the work underwriters do. In this post, I want to highlight the importance of the underwriting skillset and explore a different approach to marrying technology to that skill set which will make underwriters’ jobs easier and more effective.

Back in 2008, our survey revealed that more than 40% of underwriters’ time was spent on non-core tasks. Underwriters were struggling to move on from legacy systems and adopt new solutions. Fast forward to 2021 and the most recent survey shows that only 35% of underwriters feel that technology has decreased their workload. In 2008, that number was nearly equivalent, at 36%.

In both 2008 and 2021, a lack of data integration was cited as a challenge that accompanied new technology, with 72% of respondents in both years reporting the issue. In 2021, 79% of respondents reported that lack of process integration was the biggest reason technology negatively impacted their workload.

This data made me reflect on the day-to-day responsibilities of the underwriter and think about why technology hasn’t made the act of underwriting any easier. Today’s responses show that there’s less value placed on underwriters themselves. There’s empirical evidence for this including data showing that survey respondents largely see underwriting recruitment, training and retention programs in their organizations as deficient.

Additionally, focus on core underwriting controls and discipline is down: just 30% of an underwriter’s time is spent doing risk analysis and generating quotes. Risk analysis is the core competency of an underwriter. Their job is to review data across different sources and synthesize it to make an accurate (and profitable) decision. With this lens, I see the underwriter as the original data scientist.

The prestige and value placed on the underwriting profession has taken a dive over the last 15 years, which has left underwriters stuck with the same problems they faced over a decade ago. Insurers have prioritized minimizing expenses and “demystifying” underwriting by automating the process or decreasing the underwriter’s role in risk assessment.

We’ve done this by offloading work from the underwriters, provided new risk and pricing models to aid decision making and tried to leverage automation to make underwriting easier. None of these initiatives are negative in and of themselves. They all work well for assessing simpler, homogenous risks while driving down cost and improving pricing consistency. But they miss the fundamental issue of more complex underwriting.

The real challenge is that underwriting is still a paper-first process with important data siloed in PDFs and spreadsheets attached to emails from brokers. To assess risk, underwriters still have to move between different documents, looking for data that’s formatted in different ways depending on the broker it’s coming from.

Though we’ve tried to make the processes around underwriting easier, there hasn’t been a focus on improving the data science aspect of underwriting. This requires data to be more accessible. We need to implement solutions that help underwriters extract, manage and assess all their data in one place in a way that also provides relevant context and deeper insights.

Many organizations have made significant moves to become data-driven over the last 15 years. Insurance has always been driven by data, but it’s time to rethink how data aggregation and analysis are optimized in underwriting processes. If insurers want to see greater efficiency and improved consistency and quality in risk and pricing decisions, our focus can’t remain on offloading work from the underwriter. We need to help underwriters do what they’re best at analyzing information, uncovering patterns and making decisions based on a holistic view of an applicant.

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

1. Invest in solutions that pull all the data underwriters need out of their silos, bringing information from PDF and spreadsheet attachments into one place, eventually eliminating that mode of communication altogether.  
2. Organize information, knowledge and data around the critical underwriting decision steps of triage, risk evaluation and pricing.
3. Present information in context. For example, enable underwriters to look at new submissions compared to similar submissions to help them understand how the submission or renewal differs.
4. Integrate this data-driven, analytics-first approach into existing workflows to make the experience seamless.
5. Set up the quality controls, measures and feedback mechanisms to improve the quality and consistency of underwriting within the new process.

Luckily, we’re already seeing insurers taking steps towards improvement in this area. The 2021 survey shows that 67% of insurers will prioritize investments in underwriting platforms over the next three years. Seventy-one percent are looking to add predictive analytics to their tech stack while 66% plan to invest in customer and broker portals, another way to streamline data aggregation.

If you want to know more about how we are helping companies address these five ideas, let me know. 

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Disclaimer: This content is provided for general information purposes and is not intended to be used in place of consultation with our professional advisors.