Publications

This paper benchmarks methods for intra-year ultimate loss projections for Non-Life reserving, avoiding reliance on gross-up proxies. It compares approaches, highlights pros and cons, and offers insurers a palette of options that can be tailored to their needs and risk profile.
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The intersection of ethical considerations and predictive modeling in non-life insurance is becoming increasingly important. Today, the regulatory landscape and scientific research emphasize the need for deeper insights into non-discrimination and fairness in predictive models.

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In the finance and insurance sectors, accurately identifying outliers is vital for robust risk assessment and decision-making. Traditional methods often struggle with real-world data characterized by skewness and heavy tails. This paper introduces a tailored framework for outlier detection designed specifically for non-Gaussian distributions.

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A Reacfin Research White Paper in Machine Learning

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