3 résultats pour « Insurance »

Sample Average Approximation for Portfolio Optimization under CVaR constraint in an (re)insurance context

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“We consider optimal allocation problems with Conditional Value-At-Risk (CVaR) constraint. We prove, under very mild assumptions, the convergence of the Sample Average Approximation method (SAA) applied to this problem, and we also exhibit a convergence rate and discuss the uniqueness of the solution. These results give (re)insurers a practical solution to portfolio optimization under market regulatory constraints, i.e. a certain level of risk.”
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An AI Vision for the Actuarial Profession

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"The essay presents a vision of the AI-enhanced actuary, who leverages AI to build more accurate and efficient models, incorporates new data sources, and automates routine tasks while adhering to professional and ethical standards. We also discuss the challenges and speed-bumps along the way, including explainability, bias and discrimination risks, regulatory hurdles, and the need for actuaries to acquire AI knowledge and skills."
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Machine Learning in Forecasting Motor Insurance Claims

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Accurate insurance claims forecasting is vital for financial planning and risk management. This study introduces innovative variables, such as weather conditions and car sales, and employs Machine Learning algorithms to predict average insurance claims per quarter. Key influential variables include new car sales and minimum temperature with specific lags. The findings aid insurers in enhancing claims forecasting by considering additional parameters like weather and sales data.
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