Dynamic Credit-Collections Optimization

Chehrazi, Naveed; Weber, Thomas A.

doi:10.1287/mnsc.2018.3070

Cited by 26 publications

(16 citation statements)

References 53 publications

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“…Operations management researchers have studied the optimization of loan recovery strategies by using statistical and machine learning algorithms. For example, Chehrazi et al., (2019) developed a dynamic stochastic model to optimize the intensity of debt collections for maximizing the net value of an account. Dynamic programming models and reinforcement learning models have also been applied in optimizing the time of different debt collection actions (calls, reminder messages, and warrant) (Abe et al., 2010, De Almeida Filho et al., 2010).…”

Section: Literature Background and Hypotheses Developmentmentioning

confidence: 99%

Can Social Notifications Help to Mitigate Payment Delinquency in Online Peer‐to‐Peer Lending?

Wang

et al. 2021

Production and Operations Management

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Payment management is an operations management challenge in online peer‐to‐peer (P2P) lending. It is critical to control the cost of debt collection incurred in late repayments and defaults. We study whether and how a platform can leverage on borrowers’ social connections and use automatic social notifications in regulating repayment behavior. In collaboration with a large online P2P lending platform, we conduct a randomized field experiment to investigate the effect of social notifications targeted at different contact groups (core‐circle and peripheral‐circle groups). Our results indicate that notifying social contacts of a delinquent regarding the overdue payment significantly improves the repayment rate. Compared with the control group in which no notification messages are sent to social contacts, notification‐triggered social sanctions and social support reduce the default rate by more than 50% in both core‐circle and peripheral‐circle groups. Furthermore, we find that social notifications targeted at peripheral‐circle social contacts are only effective in the short term, and its effectiveness decreases with repeated use. By contrast, social notifications targeted at core‐circle social contacts have a lasting effect.

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Section: Literature Background and Hypotheses Developmentmentioning

confidence: 99%

Can Social Notifications Help to Mitigate Payment Delinquency in Online Peer‐to‐Peer Lending?

Wang

et al. 2021

Production and Operations Management

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“…We take 100 bootstrap replicates with the same size of the data. For each replicate, we solve the nonlinear optimization model (11) and obtain a set of coefficients. Then we calculate ∆ and T , as the inter-arrival time change and the expected sojourn time change as described in Section 4.2, for each replicate based on its fitted coefficients.…”

Section: B Statistical Significance Results Of Fitted Parametersmentioning

confidence: 99%

“…A CTMC lends itself to interpretation by associating transitions in the model with assumed phases of a client using syringes after leaving a service location. In addition to the references mentioned in Section 1, which use CTMCs with hidden states [11,27,30], we note that such an approach has also been used to model the length of stay of hospital patients [15,16,17], including work in which serial (Coxian) CTMCs are employed. We use a similar philosophy by inferring transition rates from unobservable states and, moreover, connecting them to features of a client.…”

Section: Reoccurring Visitsmentioning

confidence: 99%

Analyzing client behavior in a syringe exchange program

Yang

Morton

2019

IISE Transactions on Healthcare Systems Engineering

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Multiple syringe exchange programs serve the Chicago metropolitan area, providing support for drug users to help prevent infectious diseases. Using data from one program over a ten-year period, we study the behavior of its clients, focusing on the temporal process governing their visits to service locations and on their demographics. We construct a phase-type distribution with an affine relationship between model parameters and features of an individual client. The phase-type distribution governs inter-arrival times between reoccurring visits of each client and is informed by characteristics of a client including age, gender, ethnicity, and more. The interarrival time model is a sub-model in a simulation that we construct for the larger system, which allows us to provide a personalized prediction regarding the client's time-to-return to a service location so that better intervention decisions can be made with the help of simulation.

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“…The performance of financial institutions relies on the efficient collection of outstanding unsecured customer debt (Chehrazi et al, 2019). Among the various debt collection methods utilized in debt collection services (Du et al, 2020;Karlan et al, 2012), debt collection calls are the most widely used (Elliott, 2008;Griffith, 2009).…”

Section: Introductionmentioning

confidence: 99%

Carrots or sticks in debt collection services? A voice metrics and text analysis of debt collection calls

Liao

Yang

et al. 2021

JSTP

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PurposeAlthough phone calls are widely used by debt collection services to persuade delinquent customers to repay, few financial services studies have analyzed the unstructured voice and text data to investigate how debt collection call strategies drive customers to repay. Moreover, extant research opens the “black box” mainly through psychological theories without hard behavioral data of customers. The purpose of our study is to address this research gap.Design/methodology/approachThe authors randomly sampled 3,204 debt collection calls from a large consumer finance company in East Asia. To rule out alternative explanations for the findings, such as consumers' previous experience of being persuaded by debt collectors or repeated calls, the authors selected calls made to delinquent customers who had not been delinquent before and were being called by the company for the first time. The authors transformed the unstructured voice and textual data into structured data through automatic speech recognition (ASR), voice mining, natural language processing (NLP) and machine learning analyses.FindingsThe findings revealed that (1) both moral appeal (carrot) and social warning (stick) strategies decrease repayment time because they arouse mainly happy emotion and fear emotion, respectively; (2) the legal warning (stick) strategy backfires because of decreasing the happy emotion and triggering the anger emotion, which impedes customers' compliance; and (3) in contrast to traditional wisdom, the combination of carrot and stick fails to decrease the repayment time.Originality/valueThe findings provide a valuable and systematic understanding of the effect of carrot strategies, stick strategies and the combinations of them on repayment time. This study is among the first to empirically analyze the effectiveness of carrot strategies, stick strategies and their joint strategies on repayment time through unstructured vocal and textual data analysis. What's more, the previous studies open the “black box” through psychological mechanism. The authors firstly elucidate a behavioral mechanism for why consumers behave differently under varying debt collection strategies by utilizing ASR, NLP and vocal emotion analyses.

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Dynamic Credit-Collections Optimization

Cited by 26 publications

References 53 publications

Can Social Notifications Help to Mitigate Payment Delinquency in Online Peer‐to‐Peer Lending?

Can Social Notifications Help to Mitigate Payment Delinquency in Online Peer‐to‐Peer Lending?

Analyzing client behavior in a syringe exchange program

Carrots or sticks in debt collection services? A voice metrics and text analysis of debt collection calls

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