Poor data quality, fragmented information systems and limited access to reliable insurance records are emerging as major barriers to the adoption of artificial intelligence across Africa’s insurance industry, according to the African Insurance Organisation (AIO).
The organisation, in its 2026 annual report, said the challenges were preventing many insurers from moving beyond AI pilot projects to full operational deployment, limiting the technology’s potential to improve underwriting, risk assessment, fraud detection and claims processing.
Although African insurers are exploring AI applications, adoption remains uneven across the continent, with fragmented data infrastructure, shortages of technical expertise and regulatory uncertainties slowing progress.
The report stressed that the problem was not necessarily the volume of data available to insurers but its quality, consistency and accessibility, warning that unreliable information could undermine the accuracy of automated insurance decisions.
“An AI model trained on thin, inconsistent or unrepresentative data will systematically misprice risk,” the report stated, adding that errors arising from poor-quality data could remain undetected for years before becoming expensive to correct.
It explained that many insurers hold years of claims records in incompatible legacy systems, paper archives and formats that are difficult to integrate. Even where digital records exist, information is often scattered across product lines, distribution channels and business units.
This fragmentation makes it difficult for insurers to consolidate information and develop AI models capable of accurately assessing risks across different customer groups and insurance products.
Low insurance penetration in many African markets further compounds the problem, as relatively small pools of policyholders can leave insurers with limited historical claims data for training and testing predictive models.
The AIO said addressing these weaknesses would be critical to enabling insurers to deploy AI more effectively, particularly in underwriting, premium pricing, fraud detection and portfolio risk management.
As part of efforts to improve the industry’s data infrastructure, the organisation highlighted its co-sponsorship of the African Insurance Data Repository (AIDR), a continent-wide initiative designed to standardise and aggregate insurance statistics across markets.
According to the report, the initiative is intended to improve access to comparable insurance data, supporting more credible industry benchmarking, better-calibrated pricing models and a more consistent basis for regulatory oversight.
The organisation acknowledged, however, that a shared repository would not resolve every insurer’s internal data management challenges, although it could improve the quality and availability of information across the industry.
Beyond conventional insurance records, the AIO identified alternative data sources as another avenue for improving risk assessment, particularly in markets where formal financial records and historical insurance information are limited.
It cited satellite imagery and earth observation data as tools that have helped make agricultural insurance more viable in areas where conventional loss assessment can be expensive or impractical.
These technologies can help insurers monitor weather conditions and other environmental factors affecting agricultural risks, supporting parametric insurance products that pay out when predefined conditions or measurable triggers are met.
The report noted that parametric insurance products had been deployed at meaningful scale in Kenya, Ethiopia and Nigeria, illustrating how technology could help insurers cover risks that might otherwise be difficult or costly to assess.
It also identified mobile usage patterns, airtime purchases and mobile money transaction histories as potential sources of information for assessing customers with limited conventional financial records.
Telematics, which collects information about driving behaviour, was another example cited in the report. Such technology can enable motor insurers to assess risks using observed driving patterns rather than relying exclusively on traditional demographic variables.
However, the use of alternative data also raises questions about the accuracy, representativeness and privacy of the information used to train AI models and influence insurance decisions.
The AIO’s findings come as investment in AI-driven insurance technology expands globally. The report estimated that the global AI-in-insurance market was worth approximately $8bn in 2024 and projected that it would exceed $140bn by 2034, representing compound annual growth of more than 30 per cent.
It said investment was increasingly flowing into AI-native insurers, automated claims platforms and predictive underwriting tools.
Despite the challenges facing African markets, the organisation argued that insurers on the continent could benefit from opportunities to adopt newer technologies without having to undertake the extensive legacy-system migrations that often complicate digital transformation in more mature markets.
However, it stressed that realising this opportunity would require investment in data infrastructure, technical capabilities and appropriate governance arrangements.
The report also identified regulatory oversight as an important consideration, particularly as AI systems become more involved in decisions affecting insurance pricing, underwriting and claims.
It highlighted explainability and data privacy as key concerns, noting that insurers could face difficulties defending automated decisions if they could not adequately explain how their models arrived at particular outcomes.
The organisation added that data protection requirements in Nigeria and South Africa would be relevant to insurers seeking to deploy AI systems that rely on personal, behavioural and transactional information.
It urged insurers to incorporate audit trails, model documentation and data governance frameworks into their AI systems from the outset, rather than treating regulatory compliance as an issue to address after deployment.
The AIO warned that insurers that delay investment in data infrastructure and AI capabilities risk losing ground as competitors adopt technologies that enable faster services, more precise risk assessment and improved customer experiences.
“…African re/insurers that move deliberately now, investing in data infrastructure, developing or acquiring AI capability, and engaging regulators before the rules harden, will have a meaningful role in defining the market that emerges. Those that wait for conditions to be perfect may find that the ground has shifted, the capital has moved on, and the customers are already elsewhere,” the report noted.
