Abstract

The objective of this review was to provide an overview of new developments and innovations within the collections industry that could possible enhance the performance of collection agencies, specifically in South Africa. A literature study was conducted to determine current practices in the collections industry, as well as possible future innovations. A significant trend identified throughout the literature study was the increasing prioritisation of automated digital communication in several aspects of debt collection. It is reasonable to assume that this trend will continue to become the industry standard. Four recommendations are made based on the findings of the literature study. Firstly, South African collection agencies should investigate the feasibility of developing an app-based solution to performing collections. Secondly, collection agencies should supplement traditional modelling techniques with other tools, such as those developed in the field of machine learning. Thirdly, collection agencies could consider using speech analytics to obtain insights into call centre agents’ performance and adherence to businessrules. Lastly, the usage of social media data in collections as well as credit risk modelling in general is recommended as a topic for future study.Significance:
 
 A review of the various techniques currently employed in the field of debt collections may serve as useful reference for both academics and those working in debt collections.
 Recommendations are provided to assist businesses in aligning the operational models of their debt collection units to industry best practice.
 Topics for future research in this crucial sector of the economy, which brings together such fields as risk governance, predictive modelling, human psychology, debt management, legal compliance and business analysis, are provided.

Highlights

  • Collections agencies normally act as external debt collecting agencies for a diverse selection of institutions that may, during the normal course of their operations, encounter defaulters

  • It is usual that external debt collectors receive those debtors who have proven unresponsive to internal collection efforts

  • From a brief review of the collection process, we identified four areas that became the focus of our research: (1) innovative uses of data sources in collections; (2) innovative uses of machine learning in collections; (3) innovative ways to enhance collection strategies; and (4) innovative ways to evaluate collection strategies

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Summary

Review of innovations in the South African collection industry

The objective of this review was to provide an overview of new developments and innovations within the collections industry that could possible enhance the performance of collection agencies, in South Africa. A literature study was conducted to determine current practices in the collections industry, as well as possible future innovations. South African collection agencies should investigate the feasibility of developing an app-based solution to performing collections. Recommendations are provided to assist businesses in aligning the operational models of their debt collection units to industry best practice. Topics for future research in this crucial sector of the economy, which brings together such fields as risk governance, predictive modelling, human psychology, debt management, legal compliance and business analysis, are provided

Introduction
The collection process
Research topic
Innovative uses of data sources in collections
Possible application in collections
Data brokers
Innovative uses of machine learning in collections
Currently used in collections
Determine ability to pay Employment status Length of current employment Income
Markov chain
Text mining and speech analytics
Multivariate kernel regression
Multivariate regression
Innovative ways to enhance collection strategies
Machine learning
Optimised scheduling
Innovative ways of evaluating collection strategies
Proposals and comments
Recommendations for South African collection agencies
Innovative ways to evaluate collection strategies
Conclusions
Findings
Recommendations for future research
Full Text
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