It’s no secret that the insurance industry has a pricing tool issue. Our recent research shows that 81% of insurers aren't confident that their current pricing tech is fit for purpose. The sector’s continued reliance on Excel is part of the problem.
The flexibility and ease of use of the humble spreadsheet has made it a long-time favourite for insurance and reinsurance pricing. Some insurers have and continue to use it as the basis of impressively intricate pricing systems. But the more complicated your rating tool and underwriting requirements are, the more fragile that Excel-based system becomes, with consequences for agility and profitability.
As risks and portfolios become more complex, insurers need robust, integrated pricing tools that evolve at the speed of our market.
And so it's time to say goodbye to Excel.
Here are the top five reasons why forward-thinking insurers are choosing to consign Excel to pricing tool history.
#1: Excel is no friend to governance and standardization
To understand the integrity of your pricing tools, you need to know their history. Excel systems lack any kind of automatic data capture or version control. To track model updates and fixes, actuaries must remember to annotate their changes in a cell within the spreadsheet. Audits can become a challenge if someone forgets details or explains things poorly. It’s no surprise that almost half (47%) of pricing actuaries claim their current technology is difficult to audit and report from.
There are several challenges when it comes to validating and rolling out updated models in Excel. Few teams implement regression testing because it’s so painful to set up. Human-introduced errors easily arise from incorrect inputs and ‘quick changes’ made without appropriate review levels and oversight. And then there are the instances of underwriters operating from locally saved, outdated copies that no longer accurately reflect current underwriting appetite and strategy.
#2: Excel isn’t designed for complexity
Excel’s simplicity is one of its undeniable strengths. Except if you’re in the business of pricing complicated risks. It struggles to handle large data sets and complex models, becoming slow and prone to crashing. The more connections the Excel pricing model has to other files, the more unstable it is. Simply moving or renaming a file breaks its connection. It’s also difficult to do complex types of analysis that help insurers make strategic changes to their rating tools. Batch Testing and 'What-if' analysis require, at best, very manual workarounds.
Making pricing model logic accessible can be tough. Unlike modern, more human-readable languages such as Python, Excel formulas are not expressed in a descriptive way. It can be extremely hard to unravel how intricate formulas work if you didn’t have a hand in creating them. When the original creators leave, multi-tab, connected spreadsheet pricing models can feel like a Frankenstein of formats and formulas that new analysts are scared to touch.
#3: Lack of connectivity with your data ecosystem
Using Excel typically means your pricing tools are cut off from the rest of your organization's software ecosystem and data lake, as connecting them up is difficult to do and prone to breaking. This disconnect slows down analysis and limits the accuracy of your rating tools. Similarly, it’s rare that insurers take the time to integrate third party datasets with their Excel pricing tools.
Connected data allows you to access real-time information like risk benchmarks, inflation data, and sanctions at the point of pricing. Without it, underwriters often waste time searching for missing data, or use less of it, at the expense of better pricing decisions.
Plenty of insurers don’t see integrated data as a big priority, but as more of the market looks to gain an edge through technology, those who don’t invest in their data strategy will struggle to price risk competitively.
#4: Collaboration in Excel is clumsy at best
Excel doesn't encourage teamwork. With its limited user data capture, there's no easy way to establish an automatic feedback loop between the underwriters who use the models and the actuaries who build them. Technically, you can set this up in Excel, but again, it's complicated, resource-intensive, and often breaks, so few make the effort to.
Because it's difficult to automate or streamline the peer review process in Excel, they’re usually manual and time-consuming tasks. As a result, they tend to be completed after the risk has been written – too late to take feedback into account for the risk decision in question.
#5: Excel requires a lot of manual work
For the reasons outlined already, spreadsheet-based pricing tools require a lot of manual labour – entering data, checking it, making edits, fixing problems, the list goes on. Our study showed that underwriters spend about three hours every day just on data entry. That’s almost two days a week lost to unnecessary manual processes. Almost half of them say it takes at least a week to fill in pricing models, and one in five takes a whole month.
Every manual action not only takes time but increases the risk of errors creeping in. It is widely quoted that roughly 90% of spreadsheets contain errors, and these can lead to material consequences, both financial and reputational.
The new formula for success
The insurance industry has long found itself caught between the promise of dynamic, automated pricing tools and a stubborn reliance on legacy systems and methods. By delaying the move from old to new, insurers are risking their proficiency, and ultimately their competitiveness.
There’s good reason why few industries rely on Excel for critical data analysis and decision making in 2023. By embracing modern pricing and underwriting software, companies can uncover the marginal gains needed to outperform the market. Those who fail to act now risk seeing those margins deteriorate.
It’s time to ditch Excel.
P.S. If you’re wondering what to replace it with, check out Aviva GCS’s approach here.