An Effective bias-corrected bagging method for the valuation of large variable annuity portfolios
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<title>Effective bias-corrected bagging method for the valuation of large variable annuity portfolios</title>
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<abstract displayLabel="Summary">To evaluate a large portfolio of variable annuity (VA) contracts, many insurance companies rely on Monte Carlo simulation, which is computationally intensive. To address this computational challenge, machine learning techniques have been adopted in recent years to estimate the fair market values (FMVs) of a large number of contracts. It is shown that bootstrapped aggregation (bagging), one of the most popular machine learning algorithms, performs well in valuing VAcontracts using related attributes. In this article, we highlight the presence of prediction bias of bagging and use the bias-corrected (BC) bagging approach to reduce the bias and thus improve the predictive performance. Experimental results demonstrate the effectiveness of BC bagging as compared with bagging, boosting, and model points in terms of prediction accuracy.</abstract>
<note type="statement of responsibility">Hyukjun Gweon ,Shu Li, Rogemar Mamon</note>
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<topic>Renta vitalicia</topic>
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<topic>Anualidad variable</topic>
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<topic>Cálculo actuarial</topic>
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<topic>Matemática del seguro</topic>
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<title>Astin bulletin</title>
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<publisher>Belgium : ASTIN and AFIR Sections of the International Actuarial Association</publisher>
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<identifier type="issn">0515-0361</identifier>
<identifier type="local">MAP20077000420</identifier>
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<text>01/09/2020 Volumen 50 Número 3 - septiembre 2020 , p. 853-871</text>
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