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In general, insurers estimate reserve requirements based on top-down calculations such as the triangle-based ‘Chain Ladder’ and ‘Bornhuetter-Ferguson’ methods. While these methods offer insurers an aggregate view of loss development across their entire portfolio of risk, they are limited due to their lack of granularity. These traditional methods do not give insurers the transparency they need to discover variabilities and deviations across each individual claim where there exists unique circumstances. Estimating loss development solely through an aggregated lens puts insurers at risk of inaccurate loss reserving, where over-reserving will lead to opportunity costs for their investment strategy and under-reserving will result in severe regulatory penalties if they are unable to fulfil their fiduciary responsibility to members. In addition, the lack of transparency prevents actuaries from understanding the causes of changes that may occur between different periods of time.
AI enables you to have a more granular and accurate view of your portfolio’s risk, one that takes a bottoms-up approach, by predicting loss development on every individual claim based on their unique attributes. With the ability to analyze both structured and unstructured text, AI models apply the patterns it learns on historical claim outcomes and characteristics onto your existing claims, predicting loss developing from the initial loss all the way to 60-80-180 days into the future. By aggregating the total predicted losses, you will have a more robust estimate of your reserve requirements, preventing inefficiencies in your allocation of capital. In addition to reserving, you will also be able to incorporate more recent claims data into your pricing models by using the ultimate loss estimates given by the model.
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Insurance companies are using machine learning and AI to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from underwriting to marketing in order to make data-driven decisions to lead to increased profitability.