Significant time is spent adjusting triangles and selecting parameters when calculating IBNR reserves. However, these choices are often made using subjective “expert judgement” with limited to no scientific basis and no measure of predictive accuracy. In this session, we will demonstrate how machine learning techniques can be used to provide an automated, scientific, and rigorous basis on which to choose parameters that maximize predictive accuracy and stability of reserves. We demonstrate this framework on several real-world triangles and show that it improves predictive accuracy while ensuring reserves are adequate.
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