@inproceedings{96792e26cf96491a80d5ede7d042e3c2,
title = "Machine Learning Techniques for Variable Annuity Valuation",
abstract = "Machine learning refers to a broad class of computational methods that use experience to improve performance or to make accurate predictions. There are two broad categories of machine learning tasks: supervised learning and unsupervised learning. Supervised learning tasks involve labeled data, which consist of inputs and their desired outputs. Unsupervised learning tasks involve unlabeled data, which consist of only inputs. In this paper, we give a brief overview of some machine learning techniques and demonstrate their applications in insurance. In particular, we apply data clustering and tree-based models to address a computational problem arising from the valuation of variable annuity products. Our numerical results show that tree-based models are able to produce accurate predictions and reduce the computational time significantly.",
keywords = "data clustering, portfolio valuation, regression tree, variable annuity",
author = "Guojun Gan and Zhiyu Quan and Emiliano Valdez",
note = "Funding Information: This work is supported by a CAE (Centers of Actuarial Excellence) grant (http://actscidm.math.uconn.edu) from the Society of Actuaries. Publisher Copyright: {\textcopyright} 2018 IEEE.; 4th International Conference on Big Data and Information Analytics, BigDIA 2018 ; Conference date: 17-12-2018 Through 19-12-2018",
year = "2019",
month = jan,
day = "31",
doi = "10.1109/BigDIA.2018.8632794",
language = "English (US)",
series = "4th International Conference on Big Data and Information Analytics: Theories, Algorithms and Applications in Data Science, BigDIA 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "4th International Conference on Big Data and Information Analytics",
address = "United States",
}