Debt Collection Industry: Machine Learning Approach

Amirhossein Shoghi ()
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Amirhossein Shoghi : Tamin Andish Pars Co. Ltd.

Journal of Money and Economy, 2019, vol. 14, issue 4, 453-473

Abstract: Businesses are increasingly interested in how big data, artificial intelligence, machine learning, and predictive analytics can be used to increase revenue, lower costs, and improve their business processes. In this paper, we describe how we have developed a data-driven machine learning method to optimize the collection process for a debt collection agency. Precisely speaking, we create 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 daily 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. Precisely, 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 believe 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; Artificial Intelligence; Machine Learning; Approximate Dynamic Programming; Prescriptive Analytics (search for similar items in EconPapers)
JEL-codes: G21 G32 H63 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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