A Data Mining Framework for Valuing Large Portfolios of Variable Annuities
Guojun Gan (Department of Mathematics, University of Connecticut);Jimmy Huang (York University)
A variable annuity is a tax-deferred retirement vehicle created to address concerns that many people have about outliving their assets. In the past decade, the rapid growth of variable annuities has posed great challenges to insurance companies especially when it comes to valuing the complex guarantees embedded in these products.
In this paper, we propose a data mining framework to address the computational issue associated with the valuation of large portfolios of variable annuity contracts. The data mining framework consists of two major components: a data clustering algorithm which is used to select representative variable annuity contracts, and a regression model which is used to predict quantities of interest for the whole portfolio based on the representative contracts. A series of numerical experiments are conducted on a portfolio of synthetic variable annuity contracts to demonstrate the performance of our proposed data mining framework in terms of accuracy and speed. The experimental results show that our proposed framework is able to produce accurate estimates of various quantities of interest and can reduce the runtime significantly compared to the state-of-the-art approaches.