Explainable least square Monte Carlo for solvency capital requirement evaluation
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<title>Explainable least square Monte Carlo for solvency capital requirement evaluation</title>
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<namePart>Perla, Francesca</namePart>
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<abstract displayLabel="Summary">The article presents an explainable extension of the Least Squares Monte Carlo (LSMC) method for estimating the Solvency Capital Requirement (SCR) within the Solvency II framework. The proposed approach combines Monte Carlo simulation techniques with explainable deep learning models, specifically the localGLMnet architecture, to reduce the computational cost of nested simulations. This approach makes it possible to maintain accuracy in SCR estimation while simultaneously improving model interpretability. The results are validated through numerical experiments on realistic life insurance portfolios. In addition, the use of ElasticNet regularization is analyzed to identify the most relevant risk factors</abstract>
<note type="statement of responsibility">Francesca Perla...[et al.]</note>
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<topic>Solvencia II</topic>
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<topic>Simulación Monte Carlo</topic>
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<topic>Cálculo actuarial</topic>
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<topic>Riesgo financiero</topic>
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<topic>Seguro de vida</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20100019443">
<topic>Requerimientos financieros</topic>
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<title>North American actuarial journal</title>
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<publisher>Schaumburg : Society of Actuaries, 1997-</publisher>
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<identifier type="issn">1092-0277</identifier>
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<text>16/03/2026 Tomo 30 Número 1 - 2026 , 24 p.</text>
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