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South African Journal of Science

versión On-line ISSN 1996-7489
versión impresa ISSN 0038-2353


BLASCHCZOK, Vinzent; VERSTER, Tanja  y  BRODERICK, Alan. Review of innovations in the South African collection industry. S. Afr. j. sci. [online]. 2018, vol.114, n.7-8, pp.25-33. ISSN 1996-7489.

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 business rules. 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.

Palabras clave : debt collecting; credit risk modelling; machine learning; collection strategies; social media.

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