Data-Driven Consumer Debt Collection via Machine Learning and Approximate Dynamic Programming

32 Pages Posted: 9 Oct 2018 Last revised: 15 Oct 2018

See all articles by Ruben van de Geer

Ruben van de Geer

VU University Amsterdam - Faculty of Science - Department of Mathematics

Qingchen Wang

University of Amsterdam Business School

Sandjai Bhulai

Vrije Universiteit Amsterdam, School of Business and Economics

Date Written: September 17, 2018

Abstract

This paper develops and tests a framework for the data-driven scheduling of outbound calls made by debt collectors. These phone calls are used to persuade debtors to settle their debt, or to negotiate payment arrangements in case debtors are willing, but unable to repay. We determine on a daily basis which debtors should be called to maximize the amount of delinquent debt recovered in the long term, under the constraint that only a limited number of phone calls can be made each day. Our approach is to formulate a Markov decision process and, given its intractability, approximate the value function based on historical data through the use of state-of-the-art machine learning techniques. Specifically, we predict the likelihood with which a debtor in a particular state is going to settle its debt and use this as a proxy for the value function. Based on this value function approximation, we compute for each debtor the marginal value of making a call. This leads to a particularly straightforward optimization procedure, namely, we prioritize the debtors that have the highest marginal value per phone call. We validate our proposed methodology in a controlled field experiment conducted with real debtors. The results show that our optimized policy substantially outperforms the current scheduling policy that has been used in business practice for many years. Most importantly, our policy collects more debt in less time, whilst using substantially fewer resources — leading to a large increase in the amount of debt collected per phone call.

Keywords: debt collection, approximate dynamic programming, machine learning, prescriptive analytics

Suggested Citation

van de Geer, Ruben and Wang, Qingchen and Bhulai, Sandjai, Data-Driven Consumer Debt Collection via Machine Learning and Approximate Dynamic Programming (September 17, 2018). Available at SSRN: https://ssrn.com/abstract=3250755 or http://dx.doi.org/10.2139/ssrn.3250755

Ruben Van de Geer

VU University Amsterdam - Faculty of Science - Department of Mathematics ( email )

Amsterdam
Netherlands

Qingchen Wang (Contact Author)

University of Amsterdam Business School ( email )

Plantage Muidergracht 12
Amsterdam, 1018 TV
Netherlands

Sandjai Bhulai

Vrije Universiteit Amsterdam, School of Business and Economics ( email )

De Boelelaan 1105
Amsterdam, 1081HV
Netherlands

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