In this work, we highlight the transformative potential of Artificial Intelligence (AI), particularly Large Language Models (LLMs) and agentic AI, in the insurance sector. We consider and emphasize the unique opportunities, challenges, and potential pathways in insurance amid rapid performance improvements, increased open-source access, decreasing deployment costs, and the complexity of LLM or agentic AI frameworks. To bring it closer to home, we identify critical gaps in the African insurance market and highlight key local efforts, players, and partnership opportunities. Finally, we call upon actuaries, insurers, regulators, and tech leaders to a collaborative effort aimed at creating inclusive, sustainable, and equitable AI strategies and solutions: by and for Africans.

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We define insurtech as the use of emerging hardware, software and user interfaces to address inefficiencies or opportunities in the insurance value chain. Further, we think of the intersection of two dimensions as the focus of our discussion: industry specificity solutions and more recently established market players or solutions. Annual insurtech startup funding volumes have grown substantially since 2015. Global funding broke the $2.5 billion mark in 2015, up from an average of approximately a half- billion over the prior three years. Insurtech startup funding continued strong reaching a record $7 billion in 2020 despite the pandemic and exceeded $15 billion in 2021.

"The development of full artificial intelligence (AI) could spell the end of the human race. It would take off on its own and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete and would be superseded." Now, I love Stephen Hawking and his way of thinking. Here is a person who seems able to look around corners to predict the future. And I just don't buy this statement.

Data and analytics capabilities are becoming increasingly important'table stakes' in the property and casualty sector across Europe, North America and Asia, according to a session at the Barbados Risk and Insurance Management (BRIM) conference. Speaking at the session'Next generation insurtech: predictive modelling and artificial intelligence (AI)', Klaas Stijnen, co-founder and chief product officer at Montoux, cited NewVantage Partners' 2022 Big Data and AI Executive Survey, which found that although investment in data and AI initiatives continues to grow, achieving data-driven leadership remains an elusive goal for most organisations. Similarly, the survey found that although the take-up of AI initiatives is accelerating, the actual implementation of AI into widespread production remains low. Stijnen outlined that a data analysis-driven approach is led by data scientists, in which the focus is on available, known data and is separate from the decision-making process. Alternatively, a decision-driven approach is based on data science, which more readily challenges bias to seek missing data and is integrated into a firm's decision-making process.

To summarise, this use case presents a way for actuaries to automatically classify free-text claims causes data into pre-defined categories for further analyses. Ultimately, with the help of BERT, computers are able to understand human language. For this instance, computers are able to understand and compare medical terms or description of a claims event, which can be messy at times. The alternative which is manual filtering in Excel is not practical, especially for large number of claims. As mentioned previously, Excel has been the primary ETL tool for most life insurance actuaries.

Artificial Intelligence and machine learning have added a whole new scope for actuaries. But, it almost seems like there could be a bit of a conflict in terms of AI taking over some of what actuaries do. We caught up with Ronald Richman, chief actuary at Old Mutual Insure to get his opinion and insight into the relationship between AI and actuaries and the regulatory environment around the use of AI.

The financial services or banking industry is an essential part of our everyday lives but the institutions who adopt and integrate artificial intelligence (AI) will have a clear advantage for their future business success. Traditionally, banks provided consumers a safe and secure method of saving and storing their money, credit to buy large purchases such as homes and automobiles, and other services such as wealth management. Though the general purpose of banks and financial institutions have remained the same, the way we "bank" has changed significantly within the last few decades. With the rise of telephone and internet banking in the '80s and '90s and now with the disruption of fintechs, we've gone from going to a brick and mortar institution or ATM to "pull out cash" to a more cashless society of peer-to-peer (p2p) payments such as Venmo, PayPal, Zelle, or Cash App. We can't forget contactless payments such as Apple Pay, Google Pay, and Samsung Pay that may have you wondering if we even need banks at all.

AI, blockchain, analytics, BI, automation & emerging technologies will fundamentally change how risk is assessed, how products are developed & how customers interact with insurers. Value chains will be adjusted to accommodate, and insurers and brokers that are not only able to integrate these advances into their current value chains but also develop entirely new ways of doing business will be the leaders in the industry in the coming decades. However, even as the industry moves in this direction, the role of humans will not diminish. Instead, it will take on a more analytic, consultative and advisory role, evolving beyond pure risk evaluation. New technologies, from enhanced data capture, particularly in the personal insurance space, to risk analytics and machine learning to digital enablement and automation are expected to fundamentally change how insurance is distributed, how risk is assessed, how products are developed, and how customers interact with insurers for servicing.

With advances in cloud storage and cloud computing, actuaries are in a position to leverage their current skills to new areas within and outside of traditional roles at insurance companies. However, there is also the potential to misuse data and advanced analytical techniques. Actuaries must be aware and proficient in their understanding of the appropriate analytic techniques, data, and applications. This paper first presents an introduction to the cloud service models and their impact on the actuarial profession. It then discusses the use of the cloud in terms of financial modeling and actuarial processes, and in terms of the increased ability to collect more data to perform advanced analytics.