Intelligent Credit Monitoring: An AI-Powered Credit Monitoring Approach for Real-Time Risk Assessment in African Development Finance Institutions

Authors

Olusola Salami, University of South Africa, South Africa

Seenee Permal, University of Northampton, United Kingdom

Olusoji Akinrinade, Tshwane University of Technology, South Africa

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.TOAR3828

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

Development finance institutions (DFIs) in Africa face persistent challenges in credit risk monitoring due to delayed detection, fragmented data, and limited coverage of local-language information, exposing portfolios to elevated impairment risk and undermining developmental objectives. This paper proposes an AI-powered credit surveillance framework that integrates multilingual natural language processing (NLP), retrieval-augmented generation (RAG), and automated risk scoring aligned with IFRS 9 and Basel III requirements. The framework continuously ingests structured and unstructured data, including borrower financial statements, regulatory filings, local-language news, social media signals, and macroeconomic indicators. A proof-of-concept implementation evaluated approximately 5,000 artefacts across English, French, Swahili, Arabic and Hausa languages during a 90-day pilot. Results show a reduction in detection latency from 30 to 45 days under manual reviews to under 12 hours, improved covenant monitoring, and measurable efficiency gains for analysts. The study contributes a deployable, multilingual architecture tailored to African regulatory contexts, empirical evidence of performance improvements over traditional monitoring, and a practical roadmap for scalable, compliant credit surveillance in African DFIs. The paper presents a framework for DFI's seeking an operation platform that addresses the risk issues in a proactive manner.

Keywords

Machine Learning, Artificial Intelligence, Credit Risk, Credit Monitoring, Surveillance, Development Finance Institutions, Africa, Finance

Cite this paper

O. Salami, S. Permal and O. Akinrinade (2026) "Intelligent Credit Monitoring: An AI-Powered Credit Monitoring Approach for Real-Time Risk Assessment in African Development Finance Institutions", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.TOAR3828

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