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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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TRAIL (TRusted AI Labs) is at the forefront of advancing Trusted AI. We are excited to embark on this journey towards building trustworthy and responsible AI solutions.
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This presentation discusses the rising use of machine learning, particularly tree-based models, in non-life insurance
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Reacfin is proud to announce the launch of its new Credit Risk Practice
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31
Mar 2021
A Reacfin|Dataiku E-book about Building a Standardized Data Analytics Workflows
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Non-Life pricing: The actuarial & data science toolkit
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05
Jun 2016
Lapse rate models in life insurance and a practical method to foresee Interest Rates dependencies
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