Data is reshaping African insurance for the better; and it’s about time!

 

For too long, the insurance industry in most parts of Africa seemed stuck in the past. Think about it: you report a loss, fill out tons of paperwork, and then wait, sometimes for what feels like forever, for your claim to be processed. This outdated process isn't just frustrating for customers; it also erodes trust in insurers, especially in a region where trust can be delicate.

This lack of trust, combined with issues such as delayed claims and poor customer experiences, is a key reason why insurance remains underutilized in Africa. Across the continent, only about 2-3% of people have insurance, much lower than the global average of 7%. In Kenya, for instance, only 2.43% were covered at the end of 2024, compared to South Africa's 14.3%. Reforms are urgently needed to rebuild confidence in the industry.

The slow, costly grind of the past

At the heart of these problems are outdated manual processes. These old ways of working lead to lengthy approval times, confusing data errors across different systems, and costly rework. It's like trying to navigate a dark room with limited visibility into how claims are moving; it’s hard to see where the hold-ups are or how well the system is performing. These delays don't just annoy customers and erode trust; they also increase the insurance companies' operational costs.

Data Science to the Rescue

But now, thanks to data science, things are finally changing. Imagine insurance that can anticipate problems before they happen, automatically respond to issues, and step in early. Predictive analytics delivers on its promise as a powerful tool, transforming the customer experience from frustration to foresight.

In areas like health and motor insurance, where people value speed, openness, and personalized service, predictive analytics is becoming the key to a better customer experience. Unlike traditional methods that rely on historical claims or basic personal information to set prices, predictive analytics leverage real-time data and behavioural insights. This includes everything from data from vehicle tracking devices, how you use mobile health apps, your lifestyle choices, and even weather patterns. All this information helps insurers spot new risks and predict claims with much greater accuracy.

Real-World Examples

1. Motor Insurance: Imagine your car insurance knowing how you drive. With usage-based insurance (UBI) models, devices in your car (called telematics) can monitor your speed, how you brake, and even the time of day you travel. This allows insurers to identify high-risk drivers and proactively offer them safety tips or suggest car maintenance, ultimately reducing the chances of accidents. In some cases, the system can even automatically start the claims process the moment an accident is detected, cutting down response times and building trust.
2. Health Insurance Predictive models can identify policyholders at higher risk of hospitalization by analysing past claims, prescription refills, and even social data. Insurers can suggest preventive care such as wellness programs or early health screenings, which can reduce medical costs for both the insurer and the person insured.

But predictive analytics isn't just about spotting risks, as it directly leads to automation. Smart systems can now trigger actions the moment a possible claim is detected. For example, if a car’s telematics device registers a crash, the system can automatically record the event, check the damage using connected sensors or images, and generate an initial settlement, sometimes without any human involvement.

This smooth, automated approach, driven by predictive insights, makes operations more efficient and helps reduce fraud by catching unusual patterns early. It also meets the demands of today's consumers, who expect quick, digital services. Leading consultants like McKinsey suggest that insurers who combine predictive analytics with claims automation can cut processing costs by up to 30% and boost customer satisfaction by over 25%.

Hurdles and the Path Forward

Of course, introducing predictive analytics across Africa isn't without its challenges. Access to good-quality data remains a major hurdle. Scattered digital systems, limited internet access in rural areas, and a lack of common ways to share data can all limit the potential of these predictive models. We also need to carefully balance the ethical use of personal data with legal requirements and preserve customer trust.

Despite these challenges, the African insurance market holds immense potential. Even though many still see insurance as a luxury rather than a necessity, the rapid growth of mobile technology, digital health platforms, and ride-hailing services across Africa provide a wealth of data sources just waiting to be used.

To truly unlock this potential, insurers in the health and motor sectors should form partnerships with mobile network providers, financial technology companies, and healthcare providers. By investing in modern data infrastructure, working together across different sectors, and building models that truly put the customer first, we can finally bridge the trust gap that has held the industry back for so long.

Carolyne Nekesa ǀ Associate General Manager- Marketing ǀ Minet Kenya