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Machine Learning and AI are set to shape the future of the insurance industry significantly and most insurers are experimenting with it in some form today.  According to the Insurance Times 63% of UK insurance executives are making investments in the area (1) .  It is clear that Machine learning is promising, but its adoption in reserving has been limited. Machine learning models, particularly those with many parameters, can be difficult to interpret. Insurers need to understand why a model makes certain predictions before they rely on it to set aside millions in reserves. If a model’s predictions cannot be easily explained, regulators and stakeholders may be hesitant to trust its results. ML models require large volumes of high-quality data to function effectively. In some areas, insurers may not even have sufficient data to train models without overfitting (i.e., capturing patterns in noise rather than real trends). Reserving is heavily regulated, and any shift in methodology would likely need regulatory approval. For example, Solvency II has strict requirements on how insurers manage risk and reserves, with high standards for model transparency and validation. This makes the adoption of machine learning models challenging, as regulatory bodies may be cautious about approving “black box” models. The complexities of regulatory compliance for ML in reserving deserve a separate article of their own. Why Change when Existing Processes Work? This is a valid question, especially in an industry where reliability and stability are paramount. Traditional methods may work “well enough,” and there’s a strong argument for sticking with what’s tried and true. However, improving processes can yield significant benefits: Better Risk Assessment: Predictive analytics and advanced modeling can help insurers understand risks more precisely, potentially leading to more accurate pricing and more efficient capital allocation. Operational Efficiency: Automating certain aspects of reserving and pricing can reduce manual work, lowering operational costs and reducing the potential for human error. The decision to change isn’t about abandoning existing processes – it’s about building on them. As predictive analytics evolves, new techniques may complement traditional methods, adding layers of insight rather than replacing them entirely. What to Be Aware of When Using Predictive Analysis Predictive analytics offers powerful tools, but it also comes with challenges and risks. Regulatory Considerations: Solvency II and other regulations require insurers to manage risk in a way that’s both prudent and transparent. Any new methods for predicting reserves or assessing risk must align with regulatory standards. This often means that models need to be explainable, understandable and validated, not just accurate. Integration Challenges: Integrating advanced analytics into existing systems can be difficult, especially if those systems are legacy or heavily siloed. A phased approach – such as starting with a pilot project – can help assess the impact of new models ahead of integration.. Model Complexity vs. Simplicity: There’s a tradeoff between simple, aggregated models and highly parameterised models. More complex models may capture subtle patterns in the data, but they also risk “seeing” patterns that are just random noise. Insurers need to strike a balance between accuracy and interpretability. Understanding the Model: Predictive models should never be treated as black boxes. Before trusting a model’s output, insurers need to understand how it works, what assumptions it’s making, and where it might fall short. Expert opinion is essential for interpreting model results, and human judgment should remain a key part of the decision-making process. The Role of Expert Opinion As powerful as predictive analytics and machine learning can be, they’re not substitutes for human expertise. Insurance is a field where context and experience make all the difference – a model may provide some valuable insights, but it doesn’t understand the full picture. Insurers should resist the temptation to let AI take complete control. Instead, these tools should be seen as a way to augment expert judgment, helping to refine decisions rather than dictate them. Embracing Change with Caution Predictive analytics is changing along with the landscape of insurance, but such strength needs to be matched with prudence. Insurers that embrace predictive analytics can gain a competitive edge by offering more accurate pricing, better risk assessment, and greater operational efficiency. However, integrating these tools requires thoughtful consideration of regulatory requirements, data quality, and model interpretability. Of course, while reserving has been a focus of our discussion, these principles extend to other critical areas of insurance as well, including underwriting, pricing, and claims management where the challenges and benefits are an equally rich conversation. Markus Chong Kuan-Hui​