Batch mode active learning framework and its application on valuing large variable annuity portfolios

DOI10.1016/j.insmatheco.2021.03.008zbMath1467.91141OpenAlexW3138618557MaRDI QIDQ2038226

Hyukjun Gweon, Shu Li

Publication date: 6 July 2021

Published in: Insurance Mathematics \& Economics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.insmatheco.2021.03.008

zbMATH Keywords

machine learningactive learningvariable annuitybatch modebagged trees

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Actuarial mathematics (91G05)

Related Items (1)

Two-phase selection of representative contracts for valuation of large variable annuity portfolios

Uses Software

• AdaBoost.MH
• Latin Hypercube Sampling

Cites Work

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• Bagging predictors
• Valuation of large variable annuity portfolios: Monte Carlo simulation and synthetic datasets
• An empirical comparison of some experimental designs for the valuation of large variable annuity portfolios
• Standard errors for bagged and random forest estimators
• A decision-theoretic generalization of on-line learning and an application to boosting
• Application of data clustering and machine learning in variable annuity valuation
• Fast and efficient nested simulation for large variable annuity portfolios: a surrogate modeling approach
• A Universal Pricing Framework for Guaranteed Minimum Benefits in Variable Annuities
• Regression Modeling for the Valuation of Large Variable Annuity Portfolios
• AN EFFECTIVE BIAS-CORRECTED BAGGING METHOD FOR THE VALUATION OF LARGE VARIABLE ANNUITY PORTFOLIOS

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