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Keywords = actuarial reserving techniques

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14 pages, 458 KiB  
Article
Advancing the Use of Deep Learning in Loss Reserving: A Generalized DeepTriangle Approach
by Yining Feng and Shuanming Li
Risks 2024, 12(1), 4; https://doi.org/10.3390/risks12010004 - 26 Dec 2023
Cited by 1 | Viewed by 3652
Abstract
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 ratio to remove subjectivity [...] Read more.
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 ratio to remove subjectivity associated with premium weighting. Chain-ladder predicted outstanding claims are used as part of the multi-task learning to remove the dependence on case estimates. Grid-search is introduced for hyperparameter tuning to improve model performance. Performance-wise, the Generalized DeepTriangle outperforms both traditional chain-ladder methodology, the automated machine learning approaches (AutoML), and the original DeepTriangle model. Full article
(This article belongs to the Special Issue Machine Learning and Artificial Intelligence in Non-life Insurance: Theory, Methods and Applications)
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34 pages, 8312 KiB  
Article
Temporal Clustering of the Causes of Death for Mortality Modelling
by Nicholas Bett, Juma Kasozi and Daniel Ruturwa
Risks 2022, 10(5), 99; https://doi.org/10.3390/risks10050099 - 6 May 2022
Cited by 4 | Viewed by 3673
Abstract
Actuaries utilize demographic features such as mortality and longevity rates for pricing, valuation, and reserving life insurance and pension contracts. Capturing accurate mortality estimates requires factual mortality assumptions in mortality models. However, the dynamic and uncertain nature of mortality improvements and deteriorations necessitates [...] Read more.
Actuaries utilize demographic features such as mortality and longevity rates for pricing, valuation, and reserving life insurance and pension contracts. Capturing accurate mortality estimates requires factual mortality assumptions in mortality models. However, the dynamic and uncertain nature of mortality improvements and deteriorations necessitates better approaches in tracking mortality changes, for instance, using the causes of deaths features. This paper aims to determine temporal homogeneous clusters using unsupervised learning, a clustering approach to group causes of death based on (dis)similarity measures to set representative clusters in detection and monitoring death trends. The causes of death dataset were derived from the World Health Organization, Global Health Estimates for males and females, from 2000 to 2019, for Kenya. A hierarchical agglomerative clustering technique was implemented with modified Dynamic Time Warping distance criteria. Between 6 and 14 clusters were optimally achieved for both males and females. Using visualisations, principal clusters were detected. Over time, the causes of death trends of these clusters have demonstrated a correlated association with mortality and longevity rates, rationalizing why insurance and pension offices may include this approach as a preliminary step to undertake mortality and longevity modelling. Full article
(This article belongs to the Special Issue Longevity/Mortality Risk Measurement and Management in Actuarial Sciences)
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