Optimizing Life Insurance Solvency and Profitability in Morocco Using a Hybrid Generative and Genetic Algorithm Frameworks

Life insurance companies in Morocco, and indeed around the world, operate under a constant, high-stakes balancing act. They must generate enough profit to satisfy their investors and keep the business growing, while simultaneously holding back enough cash to guarantee they can pay every single claim their policyholders might make decades from now. This tension is not just a matter of bookkeeping; it is a structural conflict where the very actions that boost short-term earnings, such as selling more policies, can sometimes weaken the safety margins needed to survive a bad economic year. The challenge is made even harder by a changing world: economies rise and fall, stock markets fluctuate wildly, and people are living longer than ever before. When people live longer, the insurance company's obligation to pay them grows, creating a slow-moving pressure that can quietly erode financial stability. For decades, managers have tried to navigate this complex landscape using traditional tools, but these methods often struggle to handle the messy, unpredictable nature of real-world data, where one small change in the economy can ripple through the entire system in unexpected ways.

A team of researchers from Hassan II University in Casablanca has proposed a new way to solve this puzzle by combining two powerful computational approaches. They built a digital framework that acts like a sophisticated simulator, generating thousands of possible future economic scenarios—some good, some bad—to see how different insurance strategies would hold up. Instead of relying on a single "best guess" for the future, they fed these varied possibilities into an evolutionary algorithm, a type of computer program inspired by natural selection. This program does not just look for one perfect answer; it searches for a whole range of smart compromises, finding the specific mix of decisions that offers the best possible balance between making money and staying safe. The researchers tested this system using real data from the Moroccan insurance sector, covering the years 2012 to 2024, a period that included both steady growth and severe economic shocks like the global pandemic.

The results of their simulation were striking. When the researchers applied the optimized strategies suggested by their computer model to the historical data, the outcomes improved significantly compared to what actually happened in the past. The model suggested that, on average, insurers could have increased their profitability by between 20 and 40 percent while also boosting their solvency ratios—the measure of their ability to pay future claims—by a similar margin. This means that by adjusting how much they invest, how they price their policies, and how much money they set aside for future obligations, companies could theoretically be both richer and safer at the same time. The study found that the old way of doing things, which often treated these goals as separate problems, was leaving money on the table and leaving companies more vulnerable than necessary.

One of the most important discoveries was identifying exactly what drives these results. The researchers found that the profitability of life insurance in Morocco is heavily dependent on the broader economy. When the country's gross domestic product grows and the stock market performs well, insurance companies make more money, largely because their investments earn higher returns. However, the long-term safety of the company, or its solvency, is driven by different factors entirely. The most critical pressure on long-term safety comes from demographics, specifically the fact that people are living longer. As life expectancy rises, the insurance company must hold more money in reserve to pay for those extra years, a factor that is far more influential on safety than short-term inflation or market swings. This distinction is crucial because it tells managers that they cannot rely on a booming economy to fix a long-term safety problem; they must actively manage their reserves to account for the reality of an aging population.

The model also proved its worth by simulating how these optimized strategies would behave during a crisis. The researchers tested the system against a severe recession scenario, similar to the economic contraction seen in 2020, and projected how the insurance sector would fare from 2024 through 2030. Even in these harsh conditions, the strategies suggested by the model held up better than historical performance. While profitability dipped and solvency ratios declined, as they inevitably do in a downturn, the optimized approach kept the companies above the minimum safety thresholds required by regulators. This suggests that the model does not just find the best path for good times; it also builds a buffer that helps companies survive the bad ones. The simulation showed that by accumulating extra reserves during periods of economic growth, insurers could create a cushion that would allow them to weather a recession without collapsing.

The researchers also looked closely at how their computer model made its decisions, using a method that breaks down exactly which factors mattered most. They found that the model consistently identified a specific recipe for success: a precise balance between the premiums collected, the death benefits promised, the income generated from investments, and the technical provisions set aside for the future. Unlike previous studies that focused only on non-life insurance or specific types of policies, this work tackled the entire life insurance balance sheet as a single, interconnected system. The study explicitly ruled out the idea that a simple, static approach could solve the problem, showing instead that the best strategy changes depending on whether the economy is expanding or contracting. The model demonstrated that trying to maximize profit without regard for safety, or vice versa, leads to suboptimal results, whereas finding the right trade-off allows both goals to be met simultaneously.

While the findings are promising, the researchers are careful to note that these results come from a simulation based on historical data. The model assumes that the relationships between economic variables and insurance performance will continue to hold true, which may not be the case if the market faces a completely unprecedented shock. The study also acknowledges that the computer model simplifies some of the complex interactions between different types of risks. Nevertheless, the work provides a concrete, quantitative tool for insurance executives and regulators. It offers a way to visualize the trade-offs they face every day, showing them exactly how much safety they might gain if they are willing to accept a slightly different profit margin, or how much profit they could unlock by adjusting their investment strategy.

Ultimately, this research offers a new perspective on a very old problem. It suggests that the tension between making money and staying safe is not a fixed barrier that cannot be crossed, but rather a dynamic landscape that can be navigated with the right tools. By using a hybrid system that combines the ability to imagine many different futures with the power of evolutionary search, the researchers have shown that Moroccan life insurers could be more resilient and more profitable than they have been in the past. The study does not promise a magic solution that eliminates risk, but it does provide a clear, data-driven path forward for managing that risk in a world that is constantly changing. For the first time, managers in this sector have a way to see the full range of their options, allowing them to make decisions that are not just reactive to the news of the day, but strategically aligned with the long-term health of their companies.