Making the Case for Investment in Data Science within Insurance and Reinsurance: Practical Insights

At Evolution HTD, I often get asked by clients for help in sanity-checking a data science model or reviewing a project that is in the Proof of Concept (PoC) stage. These requests usually stem from a bigger question: "How do we make a compelling case for investing in data science, particularly in the insurance and reinsurance sectors?"

It’s a valid question, especially given the industry’s deep-rooted reliance on traditional models that have worked successfully for many years. Many executives understandably wonder why they should embrace change when the current business model seems perfectly fine. But here's the truth I've come to see firsthand: while traditional insurance methods are effective, they are also increasingly outdated in a world that’s becoming more data-driven every day.

Making the case for investment in data science isn’t just about introducing new tools—it's about enhancing long-standing systems, increasing efficiency, and future-proofing the business. And, as I often explain to clients, it's not about discarding what’s worked for decades, but rather, building on it to gain an even greater competitive edge in a rapidly evolving market.

Let me walk you through how I approach building a compelling case for data science investment, focusing on practical steps and real-world use cases to help validate this argument.

1. Start with the Right Use Cases: Solving Specific Pain Points

The most successful approach I’ve found is to ground the business case in specific pain points the company is facing. I don’t make vague promises about “revolutionising” the business. Instead, I hone in on well-defined problems, such as:

• Fraud Detection: I always start here. Fraudulent claims cost insurance companies billions annually, and with traditional manual checks, it’s nearly impossible to catch everything. Data science, with machine learning and pattern recognition, can automate fraud detection with far greater accuracy. I show them examples of how fraud detection algorithms can sift through vast volumes of data, finding anomalies that humans would miss.
• Customer Retention and Churn Prediction: Insurers are constantly fighting to retain customers, and understanding who is likely to leave—and why—is critical. Data science allows you to predict which customers are at risk of churning. The advantage here is that predictive models can assess factors such as customer satisfaction scores, claims history, and engagement rates, helping companies intervene before customers leave.
• Pricing Optimisation: This is a highly compelling use case. I argue that relying solely on historical data is no longer sufficient for pricing policies. Predictive models can incorporate external variables—economic trends, weather data, even behavioural patterns—allowing for dynamic pricing that adjusts in real-time.
• Claims Management: One of the biggest challenges I’ve faced when working with insurance clients is the length of time it takes to process claims. Delays are often caused by manual processes. With automation powered by machine learning, claims can be triaged more efficiently. I use examples of insurers who have cut down claim-processing times from weeks to days by automating the simpler claims, freeing up human adjusters to focus on more complex cases.

Each of these use cases directly impacts the bottom line, which is exactly where I focus my pitch. When I show how solving these problems can improve efficiency, reduce costs, and enhance customer satisfaction, it becomes easier for stakeholders to see the value in data science.

2. Build a Financial Case with Clear ROI

Insurance executives need to see numbers. They need to know how investing in data science will provide a return on investment. When I make the case, I always include a financial model showing projected ROI over time.

Here’s how I approach it:

• Initial Costs: Break down the upfront investment—whether it’s hiring data scientists, purchasing technology, or integrating new systems. Be realistic about these costs.
• Ongoing Benefits: Then I outline the tangible benefits, focusing on reduced fraud, faster claims processing, lower customer acquisition costs, and more accurate pricing models. If I can show that a small reduction in fraud could save millions over time, the numbers start to speak for themselves.
• Break-Even Point: I find that being able to calculate when the company will start to see returns is crucial. A well-designed data science initiative should show positive results within 12-18 months. If I can demonstrate that the investment pays for itself by Year 2 or Year 3, the resistance tends to soften.

3. Validate with Small Pilot Projects

One of the challenges I’ve encountered is that executives are often wary of large-scale projects that could disrupt business as usual. My solution? I advocate for starting small. I recommend launching a pilot project that targets one specific area, such as claims automation or customer segmentation. This approach allows the business to dip its toe in the water without a massive upfront commitment.

Once the pilot shows positive results—and it usually does—it becomes far easier to argue for broader investment. I’ve found that having concrete data from a successful pilot makes the conversation shift from “why should we invest?” to “how soon can we roll this out company-wide?”

4. Emphasise Compliance and Risk Management

In insurance and reinsurance, regulatory compliance and risk management are paramount. Many executives worry that moving too quickly with new technologies could put them at odds with regulatory bodies. I get it—the regulatory landscape is complex, and non-compliance can lead to hefty fines.

But this is where I make an important point: data science isn’t just about gaining a competitive edge; it’s also about reducing regulatory risk. Predictive analytics and automation can improve compliance by flagging potential issues before they become full-blown problems. And, when I present it this way, it’s often seen not just as an investment in technology, but as a proactive measure for risk mitigation.

5. Addressing the Human Factor: Change Management

Even if you have the best use cases and a solid financial case, the human factor remains a significant challenge. There’s no getting around the fact that data science initiatives often face internal resistance. People fear their jobs may be automated, or they’re simply comfortable with the status quo.

I find that the best way to manage this is through transparency and education. I emphasise that data science doesn’t replace human expertise—it enhances it. For instance, claims adjusters won’t lose their jobs to automation, but rather will be empowered to focus on more complex, rewarding work. Showing examples from other industries where similar transformations have led to greater job satisfaction can also help in getting employees on board.

Patience and Persistence Pay Off

I won’t sugarcoat it: convincing an insurance or reinsurance company to invest in data science can be a long and challenging process. The industry is cautious for good reason, and there will always be hesitations when proposing new technology in a field where the old models have worked for so long.

But the truth is, the world is changing. Consumer expectations are evolving, and competitors who embrace data science will pull ahead. In my experience, the key is to focus on solving specific problems, backing up the argument with clear ROI projections, and starting with manageable pilot projects.

It’s about showing that data science isn’t some futuristic, abstract concept—it’s a practical, necessary investment for long-term success. And if you can do that, even the most sceptical stakeholders will start to see the value.