The following was originally published in Middle East Insurance Review.
In the spring of 2025, I had the opportunity to visit the Middle East for the first time, attending a regional conference. Having spent much of my career working on AI and insurance across global markets, I was eager to engage directly with professionals from the region. I found Dubai to be a blend of tradition and cutting-edge ambition – an inspiring setting.
Over meetings with insurers, reinsurers, and underwriters, a familiar theme emerged: the challenge of making sound underwriting decisions in the face of fragmented or incomplete data.
This was not a new topic for me. Nearly a decade ago, I was part of a mortality study involving Middle Eastern data. Even then, the same issue stood out: Data was sparse, inconsistent and difficult to consolidate across different jurisdictions. At the time, we made do with traditional statistical methods and whatever datasets we could piece together. Looking back, it is clear that today’s AI technologies could have dramatically improved the insights we were able to draw from that study.
The good news is that the Middle Eastern life insurance sector, although facing these data challenges, is also poised for growth and transformation.
AI holds real promise to help overcome these hurdles, turning fragmented data into actionable insights that empower better underwriting and pricing decisions.
It is important to note that when we talk about AI in this context, we are referring to a much broader concept than just data analytics or machine learning. AI encompasses various dimensions, including image processing, speech recognition, document processing, and natural language processing. The techniques used in AI extend far beyond traditional actuarial or machine learning models, incorporating deep learning, reinforcement learning, and importantly, generative AI.
Middle Eastern life insurers often contend with gaps in data availability and quality. Health records, mortality statistics, and wellness data vary widely across countries, and there is no single, unified source of truth for underwriting decisions. This makes it difficult to assess risk accurately, leading to two undesirable outcomes: underpricing (which risks financial loss) or overpricing (which can limit market competitiveness and financial inclusion).
During the Dubai conference, these challenges were echoed by several underwriters and actuaries. Despite their deep understanding of local markets, they often work with data that does not fully reflect the populations they serve. It is a difficult balancing act, especially in a region experiencing rapid demographic and economic change.
Unlike traditional actuarial models that depend on clean, comprehensive datasets, AI can function effectively even when data is fragmented or incomplete.
Through advanced algorithms, pattern recognition, and learning from diverse data sources, AI can extract valuable insights where conventional methods fall short.
Its distinct advantages include the following:
One of AI’s main advantages is learning transfer: the ability to apply knowledge gained in one context to another. AI models trained on large global datasets, such as mortality trends or health outcomes, can be adapted for specific Middle Eastern markets by incorporating local data, even when that data is limited.
For example, a mortality prediction model developed using global datasets can be fine-tuned with Middle Eastern data to better reflect regional health trends, cultural factors, and demographics. This approach allows insurers to leverage global insights while tailoring models to the unique characteristics of their local markets.
AI also enables data augmentation: the process of enhancing limited datasets by generating synthetic data. Synthetic data mimics real-world data patterns without exposing individual identities, making it a powerful tool for improving model accuracy while respecting privacy regulations.
In the Middle East, where certain health events or population segments may be underrepresented in available datasets, synthetic data can fill in the gaps. This creates a richer dataset for training predictive models, especially for rare events like early mortality or critical illness.
Looking back at that mortality study I worked on a decade ago, this technique would have been transformative. Rather than relying solely on fragmented datasets, we could have used AI to create a broader foundation for risk assessment, improving the accuracy and confidence of our findings.
AI-powered predictive modelling can help insurers anticipate risks before they materialize. By analyzing lifestyle data, medical histories, socio-economic factors, and regional health trends, these models can generate individualized risk profiles – even when some data points are missing.
This kind of proactive approach is especially valuable in diverse and evolving markets like the Middle East. Predictive models allow insurers to adjust pricing dynamically, identify high-risk profiles early, and offer wellness programs that align with regional needs and preferences.
Generative AI has a particularly prominent role to play in the Middle Eastern insurance sector, with the potential to significantly reduce costs in areas such as administration, underwriting, and claims management. The techniques used range from Continuous Bag of Words and Skip-gram models to more advanced transformer architectures, which employ encoder/decoder techniques for generating output.
The real value lies in leveraging large language models (LLMs) in conjunction with proprietary data to generate useful insights.
This can be achieved through techniques like retrieval augmented generation (RAG), which involves embedding, vector database storing, querying, and retrieving to supplement the context provided to the LLM for meaningful predictions. Graph techniques, such as those offered by Neo4j, can also be tapped for additional insights.
The development of agents and multi-agent workflows is becoming increasingly important. These autonomous agents, designed to replace or augment human tasks, can be built and orchestrated using frameworks like LangChain. The application of these advanced AI techniques has the potential to revolutionize underwriting, claims management, and pricing in the Middle Eastern insurance market.
Of course, applying AI in life insurance comes with responsibilities. It is critical that models are transparent, explainable, and aligned with local regulations and cultural values. In markets that include takaful, ensuring that AI-driven decisions are ethical and fair becomes even more important.
At RGA, we emphasize the importance of human-in-the-loop systems, where AI augments rather than replaces human expertise. Underwriters, actuaries, and decision-makers remain central to the process, using AI tools to enhance their understanding and decision-making rather than delegating those decisions entirely to machines.
Standing beneath the towering Burj Khalifa and exploring the visionary exhibits at the Museum of the Future, I was reminded of the Middle East’s drive to lead in innovation. The same ambition exists within the region’s life insurance sector. Yet, for that ambition to succeed, innovation must be grounded in the realities of the marketplace: its data, its people, and its needs.
AI offers a bridge from fragmented data to informed decisions. By combining global best practices with regional insights, life insurers and reinsurers in the Middle East can enhance their risk assessment processes, improve pricing accuracy, and expand financial inclusion.
At RGA, we are proud to support this journey. With decades of experience in global life and health markets and a deep commitment to advancing AI responsibly, we are eager to collaborate with partners across the Middle East to help unlock the full potential of AI in life insurance.
Want to learn more about RGA's AI expertise and how our insights can benefit your organization? Let's talk.
