I’m thrilled to sit down with Simon Glairy, a renowned expert in insurance and Insurtech, whose deep knowledge of risk management and AI-driven risk assessment has shaped industry perspectives. With years of experience analyzing trends and innovations in the insurance space, Simon offers invaluable insights into the evolving world of reserving practices. In our conversation today, we explore the latest trends in how insurers manage their reserves, the impact of cutting-edge tools on actuarial precision, and the potential risks of under-reserving in the industry.
Can you walk us through the recent findings on how insurers in the United States are managing their reserves, particularly the split between conservative, aggressive, and precise approaches?
Absolutely, Benjamin. The latest analysis shows a fascinating divide among the top 20 insurers based on their reserve-to-paid-loss ratios. About 45% are taking a conservative approach, meaning they’re setting aside more funds than might be necessary to cover potential claims. This often acts as a safety net against unexpected losses. Then, 30% are more aggressive, reserving less and potentially banking on optimistic claim outcomes to boost short-term financials. The remaining 25% aim for actuarial precision, striking a balance with reserves closely aligned to expected payouts, reflecting a data-driven mindset. Each approach carries its own risks and benefits, shaped by company strategy and market pressures.
What does it mean for an insurer to adopt a conservative reserving strategy, and why are so many leaning this way?
A conservative strategy means an insurer is building a larger buffer by overestimating potential claims. This can protect against sudden spikes in losses or regulatory scrutiny, ensuring solvency even in tough times. About 45% of the top insurers are doing this, likely because of past volatility in claim trends or a cautious response to economic uncertainty. It’s a way to safeguard their financial position, though it can tie up capital that might be used elsewhere.
How would you describe an aggressive reserving stance, and what might drive insurers to choose this path?
An aggressive stance involves setting lower reserves, often below what historical data might suggest is prudent. This can make an insurer’s financials look stronger in the short term by freeing up capital for investments or other uses. Roughly 30% of top insurers are going this route, possibly due to competitive pressures or a belief that claims will develop more favorably than expected. However, it’s a riskier play—if claims exceed projections, they could face solvency issues.
Let’s dive into the innovative tool used in this analysis for property and casualty loss development. How does it help in understanding reserve behaviors?
This new tool is a game-changer for analyzing property and casualty losses. It tracks year-over-year changes in incurred versus paid losses across over 700 insurance groups. By using a heat-mapped grid and detailed drill-down tables, it categorizes insurers based on how well their reserves match up with actual paid claims. This visual and data-heavy approach provides a clear picture of whether a carrier is over- or under-reserving, helping identify trends and outliers in the industry with unprecedented clarity.
There’s been a noticeable shift toward actuarial precision in recent years. Why do you think only a quarter of top insurers are hitting this sweet spot?
That’s a great observation. Between 2019 and 2024, only 25% of the top 20 insurers achieved actuarial precision, meaning their reserve-to-paid-loss ratio falls in a tight range of 0.9 to 1.5. This suggests their assumptions and data are well-calibrated. The reason it’s not more common is likely due to the complexity of balancing caution with optimism—many insurers either err on the side of safety or push for financial flexibility. Plus, not all have adopted the advanced tools or data access needed to refine their estimates to this level.
How have advancements in technology, like data analytics and predictive modeling, changed the reserving game for insurers?
Technology has revolutionized reserving practices. Data analytics and predictive modeling allow insurers to process huge volumes of claims data in real time, spotting patterns and trends that were previously invisible. Machine learning can forecast claim development with greater accuracy, reducing guesswork. This means actuaries can set reserves more precisely, cutting down on the need for big conservative buffers and helping companies allocate capital more efficiently. It’s a shift from gut instinct to data-driven decision-making.
There’s growing concern about under-reserving in the industry. What’s behind the downward trend in reserve-to-paid-loss ratios over the past few years?
From 2020 to 2024, we’ve seen a sharp decline in reserve-to-paid-loss ratios for many insurers, often dipping below 1.0. This suggests reserves aren’t keeping pace with paid claims, which could indicate under-reserving. Some companies might be doing this intentionally to improve near-term financial results, while others may be overly optimistic about how claims will develop. It’s a worrying trend because sustained under-reserving can threaten financial stability if unexpected losses pile up.
Looking ahead, what’s your forecast for the future of reserving practices in the insurance industry?
I think we’re heading toward a more balanced landscape, Benjamin. As technology continues to advance, more insurers will likely move toward actuarial precision, driven by better data and stricter regulatory standards like IFRS 17. However, the temptation to under-reserve for short-term gains will persist, especially in competitive markets. My forecast is that we’ll see a gradual increase in precision-focused insurers over the next five to ten years, but only if the industry prioritizes long-term stability over quick wins. Vigilance and adaptation will be key.
