Aug 2026· Journal of Risk and Financial Management· 0 citations· 125 references
TL;DR
A systematic literature review of ML applications in credit risk assessment (CRA), covering publications from January 2016 to May 2026, synthesise prevailing methodologies into a unified end-to-end credit risk modelling framework spanning data preprocessing, feature engineering, model training, evaluation, and operational deployment.
Abstract
Credit risk assessment forms a cornerstone of banking risk management and the stability of the wider financial system. Over the past decade, the rapid development of machine learning (ML) techniques has substantially enhanced traditional credit risk assessment methodologies. ML has now emerged as a core technological pillar for the banking sector, strengthening risk identification capabilities, optimising credit decision-making, and advancing financial inclusion. Conventional credit scoring models, dominated by logistic regression (LR) and scorecard approaches, offer inherent strengths in interpretability and regulatory compliance. However, constrained by their linear assumptions, these methods struggle to capture complex non-linear relationships within credit data and deliver insufficient predictive accuracy for the “credit-invisible” population lacking formal credit histories. This paper presents a systematic literature review (SLR) of ML applications in credit risk assessment (CRA), covering publications from January 2016 to May 2026. A total of 894 papers were retrieved from five digital libraries, and following a rigorous multi-stage screening process, 129 studies were selected for final inclusion. Our analysis reveals that tree-based ensemble models and deep learning (DL) architectures predominate in contemporary research in this field. Meanwhile, post hoc explanation methods and machine learning operations (MLOps) are gaining significant traction as solutions to address fairness, transparency, and system maintenance challenges in real-world production environments. We synthesise prevailing methodologies into a unified end-to-end credit risk modelling framework spanning data preprocessing, feature engineering, model training, evaluation, and operational deployment. Through a critical assessment of the advantages, limitations, and inherent trade-offs of existing approaches, this SLR not only identifies current research gaps and future directions for the academic community, but also provides practical guidance for the banking sector to build compliant, fair, and efficient intelligent risk assessment systems.
The paper shows that ensemble learning models have superior predictive power and argues that behavioral data complement traditional datasets for underbanked populations, such as "credit invisibles," and makes a strong case for XAI being essential for model transparency, combating bias, and meeting regulatory requirements.
Bojun Chen· Advances in Economics, Manag...· 0 citations
This review evaluates accuracy, stability and interpretability, offering guidance for model selection in real-world credit scoring, and Logistic regression remains essential in regulated contexts requiring transparency, supporting informed decisions on balancing performance and explainability.
Radouane Aboulmaouda, Khadija Slimani, N. Chaoui· Journal of Risk and Financia...· 0 citations
It is suggested that superior ranking performance does not necessarily imply superior decision quality and that effective credit risk modeling requires balancing predictive flexibility with probabilistic reliability and governance stability.
Banking is increasingly shaped by expanding data volumes, more complex borrower behaviour, and stricter credit risk management requirements. Under such conditions, scoring models are becoming especially relevant as instruments for the formalised assessment of creditworthiness, combining analytical accuracy, speed of decision-making, and the possibility of integration into the bank’s risk management system. The study compares traditional and modern scoring models in bank credit risk management and proposes an approach to their practical use in Ukrainian banking. Its focus is on scoring models as instruments for credit risk assessment. The study combines comparative analysis, matrix modelling, simulation, statistical modelling, and machine learning methods. Given limited access to primary banking information and confidentiality requirements, the empirical analysis was conducted on a synthesised demonstration dataset designed to reflect the structure of a real retail credit portfolio. For the analysis, a sample of 1,000 observations with a default share of 22.0% was constructed, and logistic regression, discriminant analysis, Random Forest, XGBoost, and a hybrid logit + ML re-ranking model were used for comparison. The results showed that XGBoost provided the highest predictive accuracy, with an AUC-ROC of 0.861, Gini of 0.722, Recall of 0.781, and Brier score of 0.141, whereas logistic regression demonstrated an AUC-ROC of 0.781 and retained advantages in terms of interpretability and suitability for validation. The hybrid model achieved an AUC-ROC of 0.848, Gini of 0.696, Recall of 0.773, and Brier score of 0.144, thus ensuring the best balance between accuracy, explainability, calibration, and practical applicability. Practically, the study offers an adaptive approach to selecting scoring models and a matrix for evaluating them under Ukrainian banking conditions, taking into account the requirements of the regulatory environment, data quality, and the instability of the operating conditions of Ukrainian banks.
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.
Waleed Yahya Mohsen Mohammed Al-Sabri· Multidisciplinary Science Jo...· 0 citations
The rapid expansion of the gig economy in Malaysia has created new employment opportunities but has also intensified challenges related to financial inclusion, particularly access to credit and formal lending services. This study aims to systematically review the financial barriers faced by gig workers and examine the role of Machine Learning (ML) in enhancing credit risk assessment and financial accessibility for individuals engaged in non-traditional employment. A systematic literature review was conducted following the PRISMA 2020 guidelines. Relevant studies published between 2020 and 2025 were retrieved from major open-access databases, including Google Scholar, ScienceDirect, DOAJ, SpringerOpen, MDPI, and PLOS. The selected studies were analysed using thematic synthesis to identify recurring patterns, machine learning applications, data features, and research gaps related to gig workers’ creditworthiness. The findings reveal that gig workers experience significant difficulties in obtaining credit due to irregular income streams, limited employment documentation, and insufficient credit histories. This study contributes to the literature by proposing a conceptual framework that integrates machine learning techniques with risk identification, assessment, evaluation, mitigation, and monitoring processes to support more inclusive credit risk management. The framework offers a foundation for future empirical research and policy development aimed at improving financial inclusion among gig workers in Malaysia.
M. H. Bakar, S. N. Yahaya, Nurul Shahirah Mohd Hishamudin· International journal of res...· 0 citations