Does Your Insurance Analytics Program Belong in the Smithsonian?

Redefining What is Insurance Analytics Today

By Michael Schwabrow

There comes a moment when holding onto traditional methods stops being cherished and starts becoming an existential threat. A century ago, some farmers insisted on keeping horses for plowing their fields even as tractors became widely available. Don’t get me wrong, I own and love horses. Unfortunately, today, we see a similar resistance by insurers clinging to outdated analytics methods while their competitors embrace digital transformation.

A New Insurance Analytics Paradigm is Here

According to the U.S. Census of Agriculture, 1954 was the first time in history where more tractors than horses and mules were used on American farms, dropping from 25M in 1920 to 4M in 1954.

The transition from horse-drawn plows to tractors wasn’t just an upgrade – it was a complete paradigm shift that redefined what was possible in farming. Those who resisted couldn’t compete with the efficiency, scale, and precision of mechanized farming.

The insurance industry faces a similar watershed moment with data analytics. Modern analytics platforms process oceans of data to generate invaluable insights related to natural disasters, fraud, customer behavior, cybersecurity, supply chain disruptions, and other risks affecting both B2B and B2C insurance customers.

Consider this: while traditional claims processing can take 10-30 days using manual reviews and spreadsheet-based analytics, modern AI-powered systems can process straightforward claims in minutes if not faster. Similarly, underwriting decisions that once took weeks of manual data analysis can now be completed in hours—or even less—with advanced analytics platforms that automatically synthesize data from multiple sources.

Using spreadsheets or homegrown analytics solutions today is akin to transitioning from the horse to the mechanical tractor—something that should have happened long ago. Yet, surprisingly, some insurers are still making this shift.

For insurers relying on legacy systems and trusting outdated methods instead of modern analytics solutions powered by AI, the situation mirrors that of farmers who may ignore the incoming benefits of autonomous tractors powered by advanced analytics, AI, and robotics.

The False Comforts of Tradition

Just as some farmers once insisted that horses provided a superior connection to the land and better maneuverability, many business leaders today claim that their years of experience and intuition trump data-driven decision-making. These insurers argue that their Excel spreadsheets and manual processes give them more control and understanding of their operations. But like the farmers who resisted mechanization, they confuse familiarity with effectiveness.

The Cost of Falling Behind

The parallel is stark: horse-farming holdouts didn’t just operate less efficiently—they eventually went out of business. Their attachment to traditional methods couldn’t overcome the economic reality of mechanized competition. Similarly, insurers that resist modern analytics capabilities aren’t just operating less efficiently—they’re actively handicapping their ability to compete in a data-driven marketplace. Worse, they are doing a disservice to their customers and communities. This would be like a farmer preferring horses over tractors while community demand for higher yields keeps rising.

Investing in Innovation

Transitioning from horses to tractors required significant upfront investment, just as implementing modern analytics systems does today. However, those who viewed this as an insurmountable barrier rather than a necessary investment found themselves increasingly unable to compete. The same pattern is playing out today—while modern analytics platforms require an initial investment in technology and training, the cost of not upgrading is ultimately far greater.

Unlike horse farming, where the transition was absolute (very few commercial farms today use horses as their primary power source), analytics transformation allows for some nuance. However, maintaining outdated methods is not a viable long-term strategy. Just as no skill with horse-drawn equipment could match a tractor’s efficiency, no expertise with Excel spreadsheets or homegrown solutions can match the capabilities of modern analytics platforms.

There Are No Legacy Insurance Analytics Artisans

There’s an important distinction to make. While some traditional farming methods can still provide value in specialty markets (like organic farming or heritage crops), there’s no equivalent “artisanal” market for outdated analytics methods. No client will pay a premium for analysis done in Excel when automated systems provide deeper, more accurate insights in real time. The key is knowing when tradition adds value and when it merely preserves obsolescence.

Humans and Technology Are Driving This Transformation

The greatest resistance to both transformations comes from similar places: fear of change, attachment to familiar tools, and concern about becoming obsolete. Just as farm workers needed to learn to operate and maintain tractors, today’s insurance professionals need to adapt to new analytics tools. The key difference is that while the transition to tractors was optional—until it wasn’t—the move to modern analytics is already imperative, especially as AI-driven operating models become more viable.

Insurers must choose between embracing modern analytics capabilities that protect the insured against today’s risks or becoming irrelevant. The key isn’t to abandon human expertise—just as modern farmers still need to understand their land and crops—but to augment it with powerful new tools that expand what’s possible.

The future of insurance isn’t about preserving outdated methods. It’s about adopting new tools that dramatically enhance insurer intelligence and capabilities while empowering human expertise in more valuable ways. The choice is clear: evolve or become a relic of the past.