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The purpose of this paper is to survey recent developments in granular models and machine learning models for loss reserving, and to compare the two families with a view to assessment of their potential for future development. This is best understood against the context of the evolution of these...
Persistent link: https://www.econbiz.de/10012127545
Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by...
Persistent link: https://www.econbiz.de/10012015932
data across lines of business, and show that they improve on the predictive accuracy of existing stochastic methods. The …
Persistent link: https://www.econbiz.de/10012126426
This paper proposes a generalized deep learning approach for predicting claims developments for non-life insurance reserving. The generalized approach offers more flexibility and accuracy in solving actuarial reserving problems. It predicts claims outstanding weighted by exposure instead of loss...
Persistent link: https://www.econbiz.de/10014480914
The study compares model approaches in predictive modeling for claim frequency and severity within the cross-border cargo insurance domain. The aim is to identify the optimal model approach between generalized linear models (GLMs) and advanced machine learning techniques. Evaluations focus on...
Persistent link: https://www.econbiz.de/10014497395
This study explores the insurance pricing domain in the motor insurance industry, focusing on the creation of "technical models" which are essentially obtained after combining the frequency model (the expected number of claims per unit of exposure) and the severity model (the expected amount per...
Persistent link: https://www.econbiz.de/10014636529
Given the computational challenges associated with valuing large variable annuity (VA) portfolios, a variety of data mining frameworks, including metamodeling and active learning, have been proposed in recent years. Active learning, a promising alternative to metamodeling, enhances the...
Persistent link: https://www.econbiz.de/10014636846
This note revisits the ideas of the so-called semiparametric methods that we consider to be very useful when applying machine learning in insurance. To this aim, we first recall the main essence of semiparametrics like the mixing of global and local estimation and the combining of explicit...
Persistent link: https://www.econbiz.de/10012204487
Under the Solvency II regime, life insurance companies are asked to derive their solvency capital requirements from the full loss distributions over the coming year. Since the industry is currently far from being endowed with sufficient computational capacities to fully simulate these...
Persistent link: https://www.econbiz.de/10012203797
Persistent link: https://www.econbiz.de/10012204355