SINGAPORE Trends and Developments Contributed by: Carmel Green and Claire Schekeloff, RPC Premier Law
higher overall precision than a manual review alone. • AI tools should function as a “co-pilot” rather than a replacement for human judgement. Regulatory landscape around AI usage in Singapore This uptake of AI is expected to continue into 2026, but its growing role in the insurance industry has also attracted close regulatory attention. As with any emerging technology, the inherent risks associated with AI – ranging from data quality and model trans- parency to fairness and accountability – mean that the regulatory landscape around AI integration is likely to remain highly dynamic. Singapore’s current approach to regulating AI use in the financial services industry focuses on a principles- based framework, which is widely regarded as light touch yet forward-thinking and highly effective. In December 2024, the Monetary Authority of Singapore (MAS) published its “Artificial Intelligence Model Risk Management Guidelines”, which operate in conjunc- tion with earlier regulatory efforts such as the FEAT Principles and the Veritas Initiative to foster responsi- ble and transparent AI deployment across the financial services sector. More recently, the MAS announced a strategic part- nership with the UK’s Financial Conduct Authority at November’s FinTech Festival. The partnership, which is a first of its kind, is aimed at driving AI growth in the financial services industries across both jurisdic- tions, whilst also ensuring that deployment remains safe, ethical and consistent. While we await specific details about how the collaboration is expected to work, the regulatory pairing will involve the joint test- ing of AI solutions and the exchange of insights and resources, to promote responsible AI use. As the regu- lators work together on a cross-border approach to AI regulation, the joint publication of further guidance can be expected. Against this background, insurers should look to take a proactive approach to managing the flow of risks from the use of AI, including the use of practical safe- guards around how AI is deployed – eg, setting clear boundaries on when automated tools can influence underwriting or claims decisions; conducting regular
monitoring as a means of detecting any errors; and maintaining strong controls over data use and privacy. What is clear is that the regulatory landscape govern- ing the use of AI in the financial services industry in Singapore is expected to evolve rapidly over 2026 and the years that follow. Climate change and the prevalence of natural catastrophe (Nat Cat) events Physical loss In the past decade alone, extreme weather events have been responsible for over USD2 trillion in eco- nomic losses globally, as governments and industries grapple with the escalating realities of climate change and global warming. This looks set to continue, with Willis forecasting a seventh consecutive year of insured Nat Cat losses above USD100 billion. In Asia, the financial consequences of these Nat Cat events are particularly confronting. Asia is known as the epicentre of climate-related catastrophe risk, with an estimated 40% of the world’s natural disasters striking the continent. In 2025, this included the 7.7 magnitude earthquake that hit Myanmar and Thailand in March; historic wildfires in Japan and South Korea; and extreme monsoonal flooding across South-East Asia, driven by converging typhoon systems. This trend is even more damning against the backdrop of Asia’s protection gap, with an estimated 82.8% of Nat Cat losses remaining uninsured – underscoring the region’s vulnerability to Nat Cat events. Looking ahead to 2026, rising Nat Cat volatility is expected to continue to drive reinsurance pricing, tightening capacity and influencing underwriting appetites in Singapore. However, what is also becom- ing apparent is that traditional catastrophe models used by insurers to price risk are proving increasingly inadequate as climate volatility breaks from past pat- terns. In response, several of the world’s largest insur- ers are introducing forward-thinking climate analytics into their underwriting frameworks, which utilise inte- grated satellite imagery, geospatial hazard mapping, probabilistic flood and typhoon models, and long-term climate scenarios. Not only does this new technology allow for more dynamic and granular risk selection, but it also helps insurers price climate-related risks
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