Algorithmic Decisions in Debt Collection

Last registered on July 01, 2024
View Trial History

Pre-Trial

Trial Information

General Information

Title
Algorithmic Decisions in Debt Collection
RCT ID
AEARCTR-0013905
Initial registration date
June 27, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
July 01, 2024, 12:13 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

Country
Netherlands
Region

Primary Investigator

Name
Yijun Zhou
Affiliation
Baruch College
Contact Primary Investigator

Other Primary Investigator(s)

PI Name
Qingchen Wang
PI Affiliation

Additional Trial Information

Status
Completed
Start date
2020-06-01
End date
2021-01-01
Keywords
Finance & Microfinance
Additional Keywords
JEL code(s)
D14, G02, G23, G51, O33
Secondary IDs
Prior work
This trial is based on or builds upon one or more prior RCTs.
Abstract
This paper examines the role of artificial intelligence (AI) in facilitating the non-judicial collection process of delinquent consumer debt. Leveraging a randomized field experiment in the Netherlands, we show that algorithmic calling decisions achieve higher repayment rates with fewer collection calls compared with human collection officers. Uncovering the black box of AI, we find that it extracts predictive signals from unstructured notes compiled by collectors. These signals not only predict whether the delinquent borrowers would repay during the non-judicial collection process, but also shed light on the underlying motivations or impediments of delinquent borrowers' repayment behavior.
External Link(s)

Registration Citation

Citation
Wang, Qingchen and Yijun Zhou. 2024. "Algorithmic Decisions in Debt Collection." AEA RCT Registry. July 01. https://doi.org/10.1257/rct.13905-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
The experiment was independently designed and implemented by our data provider (i.e., the debt collection agency) to examine the effectiveness of algorithmic calling decisions. In June 2020, The debt collection agency randomly assigned these 7,839 borrowers into two groups. The first group, consisting of 3,885 borrowers (the control group), receives calls determined by human collection officers. The second group, comprising of 3,954 borrowers (the treated group), receives calls based algorithmic decisions during the whole collection process. Contacting/calling in both groups is made by the same team of human calling agents. In this randomized experiment, the "treatment" is defined not by whether borrowers receive calls from calling agents, but by who decide those calls. By the end of the collection process, data was collected for these borrowers' repayment behavior.
Intervention (Hidden)
Intervention Start Date
2020-06-01
Intervention End Date
2021-01-01

Primary Outcomes

Primary Outcomes (end points)
Repayment behavior of borrowers
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In June 2020, The debt collection agency randomly assigned these 7,839 borrowers into two groups. The first group, consisting of 3,885 borrowers (the control group), receives calls determined by human collection officers. The second group, comprising of 3,954 borrowers (the treated group), receives calls based algorithmic decisions during the whole collection process. Contacting/calling in both groups is made by the same team of human calling agents. In this randomized experiment, the "treatment" is defined not by whether borrowers receive calls from calling agents, but by who decide those calls. By the end of the collection process, data was collected for these borrowers' repayment behavior.
Experimental Design Details
Randomization Method
Randomization done electronically by the debt collection agency.
Randomization Unit
Randomization was at the borrower level.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
7,839 borrowers
Sample size: planned number of observations
7,839 borrowers
Sample size (or number of clusters) by treatment arms
3,954 borrowers
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials