Jul 2026· Journal of Risk and Financial Management· 0 citations· 129 references
TL;DR
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.
Abstract
With the broad application of machine learning (ML) and deep learning (DL) models in financial markets, it has become increasingly important to evaluate their performance compared with traditional methods, especially for credit risk scoring in financial services. This mechanism determines the probability of default (PD) for borrowers, in other words, how likely a client is to fail to repay their debt. With the rapid development and growing availability of ML/DL technologies, it is essential for banks, financial institutions, auditors and regulatory bodies to assess whether these approaches truly outperform traditional models in terms of accuracy, stability and interpretability or whether their complexity comes at a cost. A systematic review following PRISMA examined credit risk scoring models. From 520 initial articles, 117 were analyzed to compare ML/DL approaches with traditional methods. This review evaluates accuracy, stability and interpretability, offering guidance for model selection in real-world credit scoring. Logistic regression remains essential in regulated contexts requiring transparency, supporting informed decisions on balancing performance and explainability.
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.
Bolun Zhang, Jun Luo, Ruobing Wu et al.· 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.
An AI-based credit scoring approach that uses machine learning, deep learning, and hybrid models to improve accuracy and scalability is proposed, concluding that AI-driven credit scoring enhances smart banking, customer experience, and financial inclusion.
L. O'Connor· International Journal of Art...· 0 citations
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
Financial technology is playing an increasingly vital role in loan decision-making, and financial institutions are increasingly relying on machine learning techniques to support credit decisions. The purpose of this review is to provide a critical overview of analysis comparing the practical applicability of deep neural network (DNN) and logistic regression (LR) models within the credit scoring domain. This paper systematically collects existing studies on the application of DNN and LR models in credit scoring. The research objects include DNN and LR models, as well as their improved variants developed on the original model frameworks. On this basis, it integrates theoretical research findings with comprehensive analyses to investigate and evaluate the practicality of DNN models. The research results indicate that DNN models still exhibit significant limitations in credit scoring applications. Further model improvements or hybrid integration with other models are therefore required to enhance their practical applicability in real-world scenarios.
Shenchen Fei· Applied and Computational En...· 0 citations
This study explores the use of Explainable Artificial intelligence techniques to improve the interpretability of credit default prediction and highlights the practical value of explainable machine learning in developing more understandable, trustworthy, and accountable credit risk assessment systems for real-world financial decision-making.
Muskan, B. Sidhu· International Journal of Com...· 0 citations