Artificial Intelligence, quality data can unlock health insurance access in Africa

Irene Nafula, Head of Enterprise Business at M-TIBA. Health insurance penetration in Kenya remains below three per cent, compared to the global average of seven per cent. This reveals a critical challenge in Africa's insurance landscape. While there is plenty of data available, much of it is not digitally available, connected or fully utilised. Insurers process large volumes of data daily, yet much of it remains paper-based or fragmented across disconnected systems. These inefficiencies introduce errors up to 50 per cent of data become inaccurate by the time it's analysed-and create delays, with reporting timelines stretching from weeks to months. This fragmentation limits insurers' ability to respond quickly to market opportunities, innovate their products, or focus on customer-centric growth. To bridge this gap, the insurance sector must embrace accurate, real-time, and integrated data systems, underpinned by transformative technologies like Artificial Intelligence (AI) and Machine Learning (ML). The data foundation for AI AI has the potential to revolutionise insurance in Africa, but its effectiveness lies in the quality of the data it processes. Without accurate, connected, and secure data, even the most advanced AI tools will fall short. Building this foundation means eliminating silos and ensuring data is connected, consistent, and secure. This approach not only protects customers and insurers from risks like fraud or cyberattacks but also enables faster, more reliable decision-making. Real-world AI applications An example of the most impactful uses of AI in insurance is claims automation. AI-powered platforms, combined with human oversight, can instantly process claims while directing complex cases for further review. For example, M-TIBA's technology platform has transformed claims management for health insurers in Kenya, who report a 500 per cent increase in daily claims processing, with approval times reduced to under a minute. In contrast to traditional systems that often delayed payouts for months or even a year, claim payments are now settled within 30 to 60 days. These advancements improve operational efficiency and significantly enhance customer satisfaction, a critical factor in retaining members. Combating fraud Fraud remains one of the biggest challenges for the insurance industry. A recent report by the Insurance Regulatory Authority (IRA) revealed a 47 per cent increase in suspected fraud cases between September and December 2023. AI and ML are game changers in this space. By analysing historical data, AI can identify suspicious patterns, such as duplicate claims or inflated billing, early in the process. This proactive approach not only saves costs but also reinforces trust with members by ensuring transparency. Beyond operations: personalisation and preventive care AI enables insurers to go beyond traditional operations by creating personalised, need-based insurance products and services. These offerings make coverage more affordable and relevant, helping insurers reach populations previously excluded from the market. Moreover, AI-driven insights into health trends allow insurers to design products and services that incentivise healthier lifestyles. By spotting early indicators of illness, insurers can offer preventive measures that improve health outcomes while reducing treatment costs. This proactive approach benefits both insurers and members, creating a win-win scenario. The potential for transformation in Africa's health insurance sector is immense. By investing in quality data and technology, and embracing AI, insurers can revolutionise their operations, develop innovative products, and expand access to underserved populations. By prioritising connected, secure and actionable data, insurers can create this future - one where financial inclusion, healthier populations, and stronger economies go hand in hand, creating a lasting impact that benefits millions across Africa. -The writer is the Head of Enterprise Business at M-TIBA

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