[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123498-en":3,"doc-seo-123498-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123498,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",8,"Research & Report","Advancing Corporate Credit Risk Assessment in Emerging Markets - A Comparative Analysis of Machine Learning Classifiers in South Africa","This study examines the predictive power of machine learning techniques for corporate credit rating assessment using firm-level financial data from 208 companies across three key sectors in South Africa. Logistic regression and other classifiers—including support vector machines, random forest, decision trees, k-nearest neighbors, and XGBoost—are evaluated through accuracy, sensitivity, specificity, precision, and the Matthews correlation coefficient. Liquidity, solvency, profitability, and efficiency ratios link predictive analytics with established financial theory. Findings indicate ensemble and kernel-based methods outperform traditional baselines, especially under sectoral heterogeneity, supporting improved credit risk assessment and regulatory oversight in emerging markets.","|  | المجلة الدولية للأداء الأقتصادي\u003Cbr>International journal of economic performance\u003Cbr>ISSN: 2661-7161 EISSN:2716-9073\u003Cbr> |  |\n| --- | --- | --- |\n\nAdvancing Corporate Credit Risk Assessment in Emerging Markets: A Comparative Analysis of Machine Learning Classifiers in South Africa  \n\n| Adedeji Daniel GBADEBO  |\n| --- |\n| [gbadebo.adedejidaniel@gmail.com](gbadebo.adedejidaniel@gmail.com) |\n| Walter Sisulu University (South Africa) |\n\nSubmitted:23/10/2025 Accepted:28/11/2025 Published:20/12/2025  \nAbstract  \nThis study examines the predictive power of machine learning techniques in corporate credit rating assessment using firm-level financial data from 208 companies across three key sectors in South Africa. By employing statistical models alongside advanced classifiers, including logistic regression, support vector machines, random forest, decision trees, k-nearest neighbors, and XGBoost, the analysis evaluates model performance using accuracy, sensitivity, specificity, precision, and the Matthews correlation coefficient. The empirical design incorporates financial ratios capturing liquidity, solvency, profitability, and efficiency, thereby aligning predictive analytics with established financial theory. Results demonstrate that while traditional models provide a baseline framework, ensemble and kernel-based methods deliver superior classification accuracy, particularly when sectoral heterogeneity is considered. These findings underscore the growing role of artificial intelligence in improving credit risk assessments, enhancing financial inclusion, and supporting regulatory oversight in emerging markets. The study offers theoretical contributions to credit risk modeling and provides policy recommendations for integrating explainable machine learning into financial supervision and lending practices.  \nKeywords: Credit Risk Prediction, Machine Learning, Financial Ratios, Corporate Credit Ratings, South Africa, Ensemble Models  \nJEL Codes: C45, G21, G32, O16  \nInternational journal of economic performance/ © 2025 The Authors. Published by the University of Boumerdes, Algeria. This is an open access article under the CC BY license ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/))  \nVolume:08 Issue:02 Year:2025 P: 1  \nAdvancing Corporate Credit Risk Assessment in Emerging Markets: A Comparative Analysis of  \nMachine Learning Classifiers in South Africa  \nAdedeji Daniel GBADEBO  \nIntroduction  \nCorporate credit ratings play a pivotal role in the global financial system by shaping access to capital, influencing borrowing costs, and guiding investor decision-making. In emerging markets such as South Africa, where structural financial vulnerabilities and limited market transparency often amplify credit risk, the accuracy of rating assessments becomes particularly critical (Beck & Rojas-Suarez, 2020) . Traditional rating methodologies, while long established, have been criticized for their reliance on subjective judgments and for their inability to capture complex, nonlinear relationships inherent in financial data (Altman et al., 2020). Against this backdrop, machine learning has emerged as a transformative tool that can enhance predictive accuracy and mitigate systemic biases by leveraging computational algorithms capable of handling large, heterogeneous datasets (Dastile et al., 2020) .  \nRecent advances in artificial intelligence (AI) highlight the potential of machine learning models to complement or even substitute conventional statistical techniques in credit risk prediction. Unlike traditional approaches, machine learning classifiers such as support vector machines, random forest, and gradient boosting are designed to model nonlinear patterns, capture variable interactions, and improve generalization across diverse datasets (García et al., 2022) . This methodological shift aligns with the growing demand for more transparent and data-driven risk assessment frameworks, especially in con","cbCaiviCIPk9Hv0d","https://ap.wps.com/l/cbCaiviCIPk9Hv0d","pdf",851278,1,14,"English","en",105,"# Abstract\n# Introduction\n## Role of corporate credit ratings\n## Limits of traditional methodologies\n## Machine learning as a credit risk tool\n## Evidence from empirical studies\n## Importance of sectoral and contextual differences","[{\"question\":\"Which machine learning classifiers are used to assess corporate credit risk in South Africa?\",\"answer\":\"The study evaluates logistic regression, support vector machines, random forest, decision trees, k-nearest neighbors, and XGBoost classifiers using firm-level financial data.\"},{\"question\":\"How is model performance measured in the study?\",\"answer\":\"Performance is assessed using accuracy, sensitivity, specificity, precision, and the Matthews correlation coefficient.\"},{\"question\":\"What variables from firms are used to support credit risk prediction?\",\"answer\":\"The analysis uses financial ratios representing liquidity, solvency, profitability, and efficiency to align predictive analytics with financial theory.\"}]","Advancing Corporate Credit Risk Assessment in Emerging Markets - 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