Artificial intelligence (AI) is transforming the insurance industry by accelerating claims processing, reducing costs, improving accuracy, and enhancing customer experiences through automation, fraud detection, and data analysis.
This streamlining of workflows can help address deep-rooted skepticism toward insurers, particularly in Sub-Saharan Africa, where insurance uptake remains modest at 2–3 percent, far below the global average of 7 percent.
Globally, insurers are adopting AI for claims adjudication, fraud detection, and customer communication. However, in much of Africa, insurers remain tied to manual, paper-based systems. AI adoption faces infrastructural, regulatory, and cultural barriers despite gains in mobile penetration and digital innovation. Legacy systems, weak governance, and traditional perceptions of technology continue to slow progress. Outdated infrastructure is a key obstacle, with many insurers still storing records in file cabinets rather than in the cloud, making it difficult to produce the structured, high-quality historical data that AI models require.
In countries like Zambia and Uganda, insurers struggle with fragmented customer information stored across paper files, Excel sheets, and incompatible software. Without centralized, digitized claims data, automation remains out of reach. Cloud computing, which is essential for scalable AI, remains underutilized, especially in rural areas where slow or unreliable internet hampers real-time AI processing and remote claims assessment. This digital divide limits access to modern insurance services for the very populations that could benefit most.
Even where infrastructure exists, regulatory ambiguity is a major hurdle. Most African countries lack data protection laws aligned with global standards such as the EU’s General Data Protection Regulation (GDPR), exposing insurers to compliance risks around privacy, consent, and cross-border data sharing. AI introduces further complications as it can infer sensitive personal information, perpetuate biases, and facilitate intrusive monitoring through tools like telematics and facial recognition. In Kenya, the Data Protection Act 2019 provides a solid legal base, but uneven enforcement creates uncertainty. Without clear rules on auditing AI-driven decisions, such as claim denials, insurers risk legal disputes and reputational harm.
The absence of AI-specific legislation limits oversight by bodies such as the Insurance Regulatory Authority (IRA), causing insurers to proceed cautiously. Cultural resistance adds another layer of difficulty, where insurance employees fear that AI will displace jobs, particularly in claims assessment, underwriting, and customer service. Despite evidence that AI is more likely to complement rather than replace human roles, this fear fuels resistance to digital transformation.
Financial barriers also loom large, as implementing AI-driven claims systems requires substantial investment in software, hardware, training, and cybersecurity. For small and medium-sized underwriters operating on thin margins in competitive markets, the return on investment is uncertain. Additionally, many African insurers lack in-house expertise to manage complex AI systems, forcing them to rely on external vendors. This reliance raises concerns about vendor lock-in, accountability, and long-term sustainability, especially in the absence of a strong local InsureTech ecosystem.
On the consumer side, low trust remains a major challenge to insurance penetration. Many, especially older clients prefer face-to-face engagement and the reassurance of dealing with a human representative. In Ghana and Tanzania, customers have complained about automated claim follow-up responses, insisting on speaking to a “real person.” Mistrust of algorithmic decision-making is especially high in emotionally sensitive cases such as health or funeral claims. As a result, AI adoption in customer-facing roles is slow.
That said, AI can bring significant benefits to consumers. It can analyze policy documents, align claims with coverage terms, anticipate potential objections from insurers, and ensure that submissions are complete and well-structured. This reduces back-and-forth communication, accelerates claim resolution, and boosts operational efficiency without sacrificing accuracy.
However, even the most advanced AI solutions can fail if end-users are not ready to adopt them. Digital literacy remains low in many parts of the region, particularly among rural and older populations. Many lack access to smartphones or reliable internet, making full-scale digital claims processes difficult to implement. In Nigeria, for example, insurers that introduced mobile apps for claim reporting saw poor adoption among low-income clients, who preferred SMS or in-person visits.
This reality underscores the need to balance innovation with inclusivity. Without careful planning, AI could widen the gap between well-served urban customers and underserved rural populations.
Unlocking AI’s potential in claims management will require a multi-pronged strategy. First, insurers must digitize core systems to create centralized, high-quality data repositories. Governments need to develop and enforce AI-specific regulations that protect consumers while enabling innovation. Industry players should invest in capacity-building programs for AI, machine learning, and digital ethics across the insurance value chain.
Transparency and fairness must be central to AI deployment, while insurers should ensure algorithmic decisions are explainable and unbiased. Similarly, collaboration with local InsureTech firms can foster homegrown, scalable solutions tailored to African market realities.
The bottom line is, technology is no longer optional for the insurance industry, rather an essential driver for insurers, policyholders, and every link in the insurance value chain.
By Patrick Omoro – Associate General Manager – Minet Risk Solutions, Claims, at Minet Kenya