Old school vs. new school.
Every so often, a debate echoes down the corridor of time as the disrupters of the status quo burst on the scene with novel ways of doing things, while the established players cling to the tried and true.
This dynamic is now performing on center stage in the property/casualty insurance industry. Insurance companies and their vendors are working tirelessly to unearth newer and better data to drive more accurate financial decisions, customize product offerings and, ultimately, improve customer satisfaction. Along the way, they have begun to tap into a goldmine of vehicle data from connected cars, detailed behavioral data from phones, smart home data and analytics powered by artificial intelligence (AI) that make it possible to extract critical insights.
But for all the innovation happening on the leading edge of the industry, there is a key sticking point that’s been keeping many of these breakthroughs from reaching their fullest potential: Excel.
The fact is, much of the underlying actuarial modeling that still drives the industry today is built on the good ol’ Excel spreadsheet. That can make integrating new high-powered analytics a bit like installing Formula One parts on an Oldsmobile.
A recent survey conducted by the Casualty Actuarial Society explores that very dynamic and, unsurprisingly to those familiar with the insurance industry, the most tenured and credentialed practitioners work harder at becoming experts in spreadsheets than they do adopting cutting edge tools that can handle new data.
Shocking as that observation may be for those who’ve been following the evolution of the InsurTech space during the past several years, it makes sense. Excel has become the primary means of communication between executives on matters of importance over the course of four decades—which is the entirety of most executives’ careers. Suddenly upending that workflow is not something that’s going to happen overnight, but the industry is going to need to evolve before the cutting edge can go truly mainstream.
Classic spreadsheet models have some serious limitations that will reveal themselves clearly during this period of dynamic inflation. They’re time-smoothed, and their aggregated rows and columns cannot react quickly enough to the massive changes in price appreciation and cost differences that affect product rate adequacy, claims management, and capital demands without significant outside opinion and judgment.
Actual data exist to fill in these gaps, and many companies are finding ways to bridge that chasm using new tools, data, AI and cloud resources. For example, data from the automotive industry is now being used for simple valuation and price forecasting to address the reality that cars no longer lose value the minute they are driven off the lot. The constancy of depreciation has lulled spreadsheet modelers into a logic trap. Baked in assumptions are now violated; a portfolio of used cars can now be going up in value to an extreme instead of always falling in value each valuation period. So, for example, as used vehicle values surge to all-time highs, a $100 million in car replacement coverage two years ago may represent more than 150 percent more than that today.
Marrying these two schools of thought is easier than it appears.
For example, an underwriter could create two versions of a spreadsheet—one as usual and the other that sends vehicle VIN data in a batch for “price now” valuation. These two versions can then create scenarios of truth—one using old assumptions and one using modern data. It is likely that future prices will find equilibrium, but at a new and higher plateau. That information would change the macro view of ratemaking, reserving and capital modeling, while still being modeled in good ol’ Excel.
As anyone who has overseen an industrywide shift, there will always be those that refuse to adapt. People still do their taxes by hand, listen to vinyl albums and insist Wins are a better metric for starting pitchers than FIP. But if the past two years have shown us anything, it’s that assumptions can be wrong, and failing to be agile enough to move with the ebbs and flows that come with unforeseen volatility is what gets people left behind.
The stakes are simply too high to languish, whether it’s an old school or new school way of building out models and arriving at decisions. For insurers, it’s time to find a way to blend the best of both worlds.