Leveraging Artificial Intelligence for Enhanced Credit Risk Management: Case of Zimbabwe’s Commercial Banks

Kudakwashe Maguraushe () and Joanna Miriro Matanda
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Kudakwashe Maguraushe: Mangosuthu University of Technology
Joanna Miriro Matanda: National University Science and Technology

Chapter Chapter 15 in Sustainable Finance and Business in Sub-Saharan Africa, 2024, pp 295-311 from Springer

Abstract: Abstract The study examines the impact of artificial intelligence (AI) on credit risk prediction within Zimbabwe’s commercial banking sector, a critical area with significant implications for the sector’s financial stability and the broader economy. The research focuses on the application of AI tools, specifically machine learning (ML), robotic process automation (RPA), and natural language processing (NLP), in mitigating the risks associated with non-performing loans. Adopting a pragmatic approach with an explanatory research design, the study gathered data through self-administered questionnaires and interviews. This data was subjected to logit regression analysis and descriptive statistics using SPSS version 22. The results indicate that ML, by enabling computer systems to learn from data autonomously, significantly enhances the accuracy of credit risk prediction. This outcome suggests that AI applications can provide a transformative solution for credit risk management in banks. The study recommends the adoption of AI tools, emphasising the necessity for continual training and skill development of credit risk management personnel. This is vital due to the complex nature of AI data interpretation, which demands sophisticated and evolving skill sets to minimise decision bias and maximise the benefits of AI in credit risk management.

Keywords: Artificial intelligence; Credit risk prediction; Non-performing loans; Machine learning; Robotic process automation; Natural language processing; Logit regression analysis (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-74050-3_15

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DOI: 10.1007/978-3-031-74050-3_15

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