By By CAROLYNE NEKESA (Source: BD)
For too long, the insurance industry in much of Africa has 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This outdated process isn’t just frustrating for customers; it also causes people to distrust insurers, especially in a region where trust can be delicate. This lack of trust, along with issues like delayed claims and poor customer experiences, is a big reason why insurance isn’t as widely used in Africa as it could be.
Across the continent, only about 2-3 percent of people have insurance, much lower than the global average of 7 percent. In Kenya, for instance, only 2.43 percent were covered at the end of 2024, compared to South Africa’s 14.3 percent. 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At the heart of these problems are outdated manual processes.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, create confusing data errors across different systems, and often mean 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 push up operational costs for the insurance companies themselves.
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. This is the promise of predictive analytics, a powerful tool that is transforming the customer experience from one of frustration to one of foresight. In areas like health and motor insurance, where people really value speed, openness, and personalised service, predictive analytics is becoming the key to a better customer experience.
Unlike traditional methods that look at old data like claims history or basic personal details to set prices, predictive analytics use real-time information and behavioral data. 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
• 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.
• Health Insurance: Predictive models are also being used to identify policyholders who might be at higher risk of needing hospitalization, based on their past claims, prescription refills, and even social data. This means insurers can suggest preventive care like 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; 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 even 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 combining predictive analytics with automation in claims can cut processing costs by up to 30 percent and boost customer satisfaction by over 25 percent.
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 for sharing data can all limit how much these predictive models can do. We also need to carefully balance the ethical use of personal data with legal requirements and maintain 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 provides 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 each other, 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.
The writer is the Head of Marketing at Minet Kenya