Credit risk modeling in emerging markets: A comparative analysis of traditional and cash-flow-focused approaches — evidence from Yemen

Abstract

This study addresses the critical challenge of credit risk assessment in data-scarce emerging and frontier markets by developing and empirically validating an enhanced eight-criterion (8C) cash-flow-centric credit scoring framework. Traditional default prediction models rely heavily on standardized financial statements and transparent accounting systems—conditions that are often absent in fragile institutional environments such as Yemen, where information asymmetry and institutional weaknesses complicate objective credit evaluation. Using a unique hand-collected dataset of 40 complete corporate credit files from the Cooperative and Agricultural Credit Bank (2010–2019), this study conducts a comparative empirical analysis between the incumbent 7C heuristic framework and the proposed 8C model. The methodology employs linear discriminant analysis and binary logistic regression to evaluate predictive accuracy, classification stability, and explanatory power within a small-sample frontier setting. The results demonstrate that the 8C model substantially outperforms the traditional 7C approach, achieving an overall classification accuracy of 92.5% compared to 77.5%, while reducing False Negative (false acceptance of defaulters) from 33.3% to 11.1%. The area under the ROC curve (AUC) increases from 0.74 to 0.94, indicating strong discriminatory power and improved risk differentiation capacity. Logistic regression results confirm that cash-flow-based repayment capacity and borrower character are the strongest predictors of default, whereas collateral shows no statistically significant explanatory power in this frontier context. The findings provide empirical support for shifting from collateral-heavy lending practices toward forward-looking, cash-flow-sensitive underwriting models aligned with IFRS 9 and Basel III principles. The study contributes theoretically, methodologically, and practically by offering a ready-to-implement framework specifically designed for data-constrained frontier banking systems and institutional environments characterized by limited financial transparency.