Artificial intelligence (AI) is revolutionizing insurance by accelerating claims processing, reducing costs, improving accuracy, and enhancing customer interactions through automation, fraud detection, and data analysis.
AI streamlines workflows and improves the customer experience, which in turn addresses widespread scepticism toward insurers, especially in Sub-Saharan Africa, where trust is fragile and insurance uptake remains modest. Insurance penetration across Africa averages just 2–3 percent, far below the global average of 7 percent.
While global insurers are increasingly leveraging AI to automate claims adjudication, fraud detection, and customer communication, most insurers across the continent remain stuck in manual, paper-based processes.
The challenge lies in a complex web of infrastructural, regulatory, and cultural barriers that continue to stall AI adoption in claims management across the region. Despite promising gains in mobile penetration and digital innovation, systemic gaps remain, many rooted in legacy systems, weak governance, and social perceptions of technology.
At the heart of the problem is an outdated infrastructure with manual records stored in file cabinets, rather than cloud systems. This makes it difficult to train AI models, which require structured, high-quality historical data.
For instance, in Zambia and parts of Uganda, insurers struggle with fragmented customer information spread across paper files, Excel sheets, and incompatible software systems. Without centralized and digitized claims data, automation remains a distant dream.
Moreover, cloud computing, a key enabler of AI deployment, remains underutilized. Rural and underserved areas have limited access to reliable, high-speed internet, which hampers real-time AI processing and remote claims assessment. This digital divide deepens existing inequalities in access to insurance services, especially for populations that stand to benefit most from innovation.
But even when infrastructure is in place, insurers face regulatory ambiguity that fuels caution. Most countries in Sub-Saharan Africa lack comprehensive data protection laws aligned with global standards such as the General Data Protection Regulation (GDPR). This exposes insurers to compliance risks, particularly around data privacy, customer consent, and cross-border data sharing. Additionally, an even greater challenge comes from AI's capacity to analyse and infer sensitive information from data, perpetuate biases, and its unauthorized monitoring, which pose significant risks to anonymity and freedom. For a moment, think about using AI in surveillance telematics and facial recognition; these technologies have a far-reaching impact on privacy. In Kenya, for instance, while the Data Protection Act 2019 has laid a strong foundation to protect personal data, enforcement remains uneven, leaving insurers uncertain about how to implement AI ethically and lawfully. Without clear rules on how to audit AI-driven decisions, such as denying a claim or appealing outcomes, insurers risk legal battles and reputational damage.
Adding to this is the absence of AI-specific legislation, making it difficult for a regulatory body like the Insurance Regulatory Authority (IRA) to supervise automated claims processes effectively. Until governments catch up with the pace of technological change, insurance firms will continue to proceed with caution.
AI’s biggest roadblock, however, may not be technical at all, but cultural. Many employees in insurance firms fear that AI will displace jobs, particularly among claims assessors, underwriters, and customer service teams. This fear breeds resistance to digital transformation, despite evidence that AI is more likely to augment human roles than replace them entirely.
Insurers also face financial constraints. Implementing AI-driven claims platforms requires heavy investment in software, hardware, training, and cybersecurity. For small and medium-sized underwriters already grappling with thin margins and intense competition, the return on investment is not immediately clear.
Moreover, many African insurers lack the in-house capacity to manage complex AI ecosystems and rely on external vendors, raising concerns about vendor lock-in, accountability, and long-term sustainability. Without a robust ecosystem of local InsureTech players and talent, AI risks becoming another imported solution that fails to take root locally.
On the consumer side, trust remains low, which is a key contributor to the generally low insurance uptake and penetration in sub-Saharan Africa. Insured clients, particularly older populations, still value face-to-face interaction and the reassurance of a human being handling their case. In Ghana and Tanzania, complaints have surfaced around automated responses during claim follow-ups, with customers demanding to speak to a “real person". Mistrust in algorithmic decisions, especially in emotionally charged claims like health or funeral coverage, slows down AI deployment in customer-facing roles. But it is not all gloomy to the insuring public. AI can review the insured’s policy documents while matching them against the claim scenario, frame the claim within the boundaries of coverage, anticipate and mitigate possible insurer objections, and ensure that claims submissions are complete and well-structured to reduce back-and-forth with the insurer or loss adjuster.
Even the most sophisticated AI system will fail if end users are not ready. Across the region, digital literacy remains low, particularly among rural and older populations. Many still lack access to smartphones or reliable internet connections, making it difficult to roll out end-to-end digital claims processes. In Nigeria, for instance, some insurance companies tried deploying mobile apps for claim reporting and found that uptake was poor among low-income clients who prefer SMS or in-person visits.
The insurance value chain should balance innovation and inclusivity, ensuring that AI does not further marginalize already underserved populations.
To unlock the full potential of AI in claims management, Sub-Saharan Africa must adopt a multi-pronged approach, including digitized core systems and the development of clear legal frameworks through AI-specific guidelines that both protect consumers and foster innovation. Further potential can be unlocked by building capacity through AI training programs, machine learning, and digital ethics across the insurance value chain. To foster trust, insurers must ensure algorithmic decisions are explainable, fair, and transparent while promoting InsureTech collaboration to drive localized, scalable solutions.
The bottom line is that technology is no longer optional for the insurance industry — it has become a necessity for insurers, policyholders, and the entire insurance value chain.
Patrick Omoro ǀ Associate General Manager – MRS Claims, General Insurance ǀ Minet Kenya
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