2010 OptimizingDebtCollectionsUsingC

• (Abe et al., 2010) ⇒ Naoki Abe, Prem Melville, Cezar Pendus, Chandan K. Reddy, David L. Jensen, Vince P. Thomas, James J. Bennett, Gary F. Anderson, Brent R. Cooley, Melissa Kowalczyk, Mark Domick, and Timothy Gardinier. (2010). “Optimizing Debt Collections Using Constrained Reinforcement Learning.” In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2010). doi:10.1145/1835804.1835817

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• http://scholar.google.com/scholar?q=%22Optimizing+debt+collections+using+constrained+reinforcement+learning%22+2010
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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).

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 OptimizingDebtCollectionsUsingC	Naoki Abe Prem Melville Cezar Pendus Chandan K. Reddy David L. Jensen Vince P. Thomas James J. Bennett Gary F. Anderson Brent R. Cooley Melissa Kowalczyk Mark Domick Timothy Gardinier			Optimizing Debt Collections Using Constrained Reinforcement Learning		KDD-2010 Proceedings		10.1145/1835804.1835817		2010