Abstract

The problem of optimally managing the collections process by taxation authorities is one of prime importance, not only for the revenue it brings but also as a means to administer a fair taxing system. The analogous problem of debt collections management in the private sector, such as banks and credit card companies, is also increasingly gaining attention. With the recent successes in the applications of data analytics and optimization to various business areas, the question arises to what extent such collections processes can be improved by use of leading edge data modeling and optimization techniques. In this paper, we propose and develop a novel approach to this problem based on the framework of constrained Markov Decision Process (MDP), and report on our experience in an actual deployment of a tax collections optimization system at New York State Department of Taxation and Finance (NYS DTF). The tax/debt collections process is complex in nature and its optimal management will need to take into account a variety of considerations. At a high level, the collections process management problem is that of determining the answers to the following quesAppearing in Proceedings of the 28 th International Conference on Machine Learning, Bellevue, WA, USA, 2011. Copyright 2011 by the author(s)/owner(s).