Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

Ahmed, S., Alshater, M. M., Ammari, A. El, & Hammami, H. (2022). Artificial intelligence and machine learning in finance: A bibliometric review. Research in International Business and Finance, 61. https://doi.org/10.1016/j.ribaf.2022.101646

Ariza-Garzon, M. J., Arroyo, J., Caparrini, A., & Segovia-Vargas, M. J. (2020). Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending. IEEE Access, 8, 64873–64890. https://doi.org/10.1109/ACCESS.2020.2984412.

Ariza-Garzón, M. J., Camacho-Miñano, M. D. M., Segovia-Vargas, M. J., & Arroyo, J. (2021). Risk-return modelling in the p2p lending market: Trends, gaps, recommendations and future directions. Electronic Commerce Research and Applications, 49. https://doi.org/10.1016/j.elerap.2021.101079.

Assous, H. F. (2022). Prediction of Banks Efficiency Using Feature Selection Method: Comparison between Selected Machine Learning Models. Complexity, 2022. https://doi.org/10.1155/2022/3374489

Babaei, G. & Bamdad, S. (2023). Application of credit-scoring methods in a decision support system of investment for peer-to-peer lending. International Transactions in Operational Research, 30(5), pp. 2359–2373. https://doi.org/10.1111/itor.13064.

Bastos, J. A. (2022). Predicting Credit Scores with Boosted Decision Trees. Forecasting, 4(4), 925–935. https://doi.org/10.3390/forecast4040050.

Bastos, J. A. & Matos, S. M. (2022). Explainable models of credit losses. European Journal of Operational Research, 301(1), pp. 386–394. https://doi.org/10.1016/j.ejor.2021.11.009.

Bellotti, A., Brigo, D., Gambetti, P. & Vrins, F. (2021). Forecasting recovery rates on non-performing loans with machine learning. International Journal of Forecasting, 37(1), 428–444. https://doi.org/10.1016/j.ijforecast.2020.06.009.

Bhattacharya, A., Biswas, S. K. & Mandal, A. (2023). Credit risk evaluation: a comprehensive study. Multimedia Tools and Applications, 82(12), 18217–18267. https://doi.org/10.1007/s11042-022-13952-3.

Bitetto, A., Cerchiello, P., & Mertzanis, C. (2023). Measuring financial soundness around the world: A machine learning approach. International Review of Financial Analysis, 85. https://doi.org/10.1016/j.irfa.2022.102451.

Boguslauskas, V., Mileris, R., & Adlyte, R. (2011). New internal rating approach for credit risk assessment. Technological and Economic Development of Economy, 17(2), 369–381. https://doi.org/10.3846/20294913.2011.583721.

Çallı, B. A., & Coşkun, E. (2021). A Longitudinal Systematic Review of Credit Risk Assessment and Credit Default Predictors. SAGE Open, 11(4). https://doi.org/10.1177/21582440211061333.

Chang, Y. C., Chang, K. H. and Wu, G. J. (2018). Application of eXtreme gradient boosting trees in the construction of credit risk assessment models for financial institutions. Applied Soft Computing Journal, 73, 914–920. https://doi.org/10.1016/j.asoc.2018.09.029.

Chen, N., Ribeiro, B. & Chen, A. (2016). Financial credit risk assessment: a recent review. Artificial Intelligence Review, 45(1), 1–23. https://doi.org/10.1007/s10462-015-9434-x.

Chen, S., Härdle, W. K. & Moro, R. A. (2011). Modeling default risk with support vector machines. Quantitative Finance, 11(1), 135–154. https://doi.org/10.1080/14697680903410015.

Ciampi, F., Cillo, V., & Fiano, F. (2020). Combining Kohonen maps and prior payment behavior for small enterprise default prediction. Small Business Economics, 54(4), 1007–1039. https://doi.org/10.1007/s11187-018-0117-2.

Corazza, M., De March, D., & di Tollo, G. (2021). Design of adaptive Elman networks for credit risk assessment. Quantitative Finance, 21(2), 323–340. https://doi.org/10.1080/14697688.2020.1778175.

de Castro Vieira, J. R., Barboza, F., Sobreiro, V. A., & Kimura, H. (2019). Machine learning models for credit analysis improvements: Predicting low-income families’ default. Applied Soft Computing Journal, 83. https://doi.org/10.1016/j.asoc.2019.105640.

Djeundje, V. B., & Crook, J. (2022). Sensitivity of stress testing metrics to estimation risk, account behaviour and volatility for credit defaults. Journal of the Operational Research Society, 74(7), 1763–1774. https://doi.org/10.1080/01605682.2022.2115413.

Feki, A., Ishak, A. Ben, & Feki, S. (2012). Feature selection using Bayesian and multiclass Support Vector Machines approaches: Application to bank risk prediction. Expert Systems with Applications, 39(3), 3087–3099. https://doi.org/10.1016/j.eswa.2011.08.172.

Feldman, D. & Gross, S. (2005). Mortgage default: Classification trees analysis. Journal of Real Estate Finance and Economics, 30(4), 369–396. https://doi.org/10.1007/s11146-005-7013-7.

Fitzpatrick, T., & Mues, C. (2021). How can lenders prosper? Comparing machine learning approaches to identify profitable peer-to-peer loan investments. European Journal of Operational Research, 294(2), 711–722. https://doi.org/10.1016/j.ejor.2021.01.047.

Florez-Lopez, R. & Ramon-Jeronimo, J. M. (2015). Enhancing accuracy and interpretability of ensemble strategies in credit risk assessment. A correlated-adjusted decision forest proposal. Expert Systems with Applications, 42(13), 5737–5753. https://doi.org/10.1016/j.eswa.2015.02.042.

Giudici, P., Centurelli, M. & Turchetta, S. (2024). Artificial Intelligence risk measurement. Expert Systems with Applications, 235(August 2023), 121220. https://doi.org/10.1016/j.eswa.2023.121220.

Guo, Y. (2020). Credit risk assessment of P2P lending platform towards big data based on BP neural network. Journal of Visual Communication and Image Representation, 71, 102730. https://doi.org/10.1016/j.jvcir.2019.102730.

Guo, Y., Jiang, S., Qiao, H., Chen, F., & Li, Y. (2021). A new integrated similarity measure for enhancing instance-based credit assessment in P2P lending. Expert Systems with Applications, 175, 114798. https://doi.org/10.1016/j.eswa.2021.114798.

Guo, Y., Jiang, S., Zhou, W., Luo, C. & Xiong H. (2021). A predictive indicator using lender composition for loan evaluation in P2P lending. Financial Innovation, 7(1), 1–24. https://doi.org/10.1186/s40854-021-00261-1.

Härdle, W., Lee, Y. J., Schäfer, D., & Yeh, Y. R. (2009). Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies. Journal of Forecasting, 28(6), 512–534. https://doi.org/10.1002/for.1109.

Hughes, J. P., Jagtiani, J., & Moon, C. G. (2022). Consumer lending efficiency: commercial banks versus a fintech lender. Financial Innovation, 8(1), 1–39. https://doi.org/10.1186/s40854-021-00326-1.

Jiang, C., Xiong, W., Xu, Q., & Liu, Y. (2021). Predicting default of listed companies in mainland China via U-MIDAS Logit model with group lasso penalty. Finance Research Letters, 38, 101487. https://doi.org/10.1016/j.frl.2020.101487.

Jiang, J., Meng, X., Liu, Y., & Wang, H. (2022). An Enhanced TSA-MLP Model for Identifying Credit Default Problems. SAGE Open, 12(2). https://doi.org/10.1177/21582440221094586.

Kaposty, F., Kriebel, J., & Löderbusch, M. (2020). Predicting loss given default in leasing: A closer look at models and variable selection. International Journal of Forecasting, 36(2), 248–266. https://doi.org/10.1016/j.ijforecast.2019.05.009.

Kellner, R., Nagl, M., & Rösch, D. (2022). Opening the black box – Quantile neural networks for loss given default prediction. Journal of Banking and Finance, 134, 106334. https://doi.org/10.1016/j.jbankfin.2021.106334.

Khemakhem, S., & Boujelbene, Y. (2018). Predicting credit risk on the basis of financial and non-financial variables and data mining. Review of Accounting and Finance, 17(3), 316–340. https://doi.org/10.1108/RAF-07-2017-0143.

Kim, H. S., & Sohn, S. Y. (2010). Support vector machines for default prediction of SMEs based on technology credit. European Journal of Operational Research, 201(3), 838–846. https://doi.org/10.1016/j.ejor.2009.03.036.

Kočenda, E., & Iwasaki, I. (2022). Bank survival around the world: A meta-analytic review. Journal of Economic Surveys, 36(1), 108–156. https://doi.org/10.1111/joes.12451.

Kolte, A., Roy, J. K., & Vasa, L. (2023). The impact of unpredictable resource prices and equity volatility in advanced and emerging economies: An econometric and machine learning approach. Resources Policy, 80. https://doi.org/10.1016/j.resourpol.2022.103216.

Korangi, K., Mues, C., & Bravo, C. (2023). A transformer-based model for default prediction in mid-cap corporate markets. European Journal of Operational Research, 308(1), 306–320. https://doi.org/10.1016/j.ejor.2022.10.032.

Kou, G., Peng, Y., & Lu, C. (2014). MCDM approach to evaluating bank loan default models. Technological and Economic Development of Economy, 20(2), 292–311. https://doi.org/10.3846/20294913.2014.913275.

Kristóf, T., & Virág, M. (2022). EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks. Research in International Business and Finance, 61. https://doi.org/10.1016/j.ribaf.2022.101644

Kruppa, J., Schwarz, A., Arminger, G. & Ziegler, A. (2013). Consumer credit risk: Individual probability estimates using machine learning. Expert Systems with Applications, 40(13), 5125–5131. https://doi.org/10.1016/j.eswa.2013.03.019.

Li, B. (2022). Online Loan Default Prediction Model Based on Deep Learning Neural Network. Computational Intelligence and Neuroscience, 2022, 1–9. https://doi.org/10.1155/2023/9808494.

Li, G., Wang, X., Bi, D., & Hou, J. (2022). Risk Measurement of the Financial Credit Industry Driven by Data: Based on DAE-LSTM Deep Learning Algorithm. Journal of Global Information Management, 30(11). https://doi.org/10.4018/JGIM.308806.

Li, S. T., Shiue, W., & Huang, M. H. (2006). The evaluation of consumer loans using support vector machines. Expert Systems with Applications, 30(4), 772–782. https://doi.org/10.1016/j.eswa.2005.07.041.

Li, W., Ding, S., Chen, Y. & Yang, S (2018). Heterogeneous ensemble for default prediction of peer-to-peer lending in China. IEEE Access, 6, 54396–54406. https://doi.org/10.1109/ACCESS.2018.2810864.

Li, Z., Jiang, Z. & Pan, X. (2022). Default Risk Prediction of Enterprises Based on Convolutional Neural Network in the Age of Big Data: Analysis from the Viewpoint of Different Balance Ratios. Complexity, 2022. https://doi.org/10.1155/2022/5139562.

Lin, C., Qiao, N., Zhang, W., Li, Y. & Ma, S. (2022). Default risk prediction and feature extraction using a penalized deep neural network. Statistics and Computing, 32(5), 1–17. https://doi.org/10.1007/s11222-022-10140-z.

Lin, S. L. (2009). A new two-stage hybrid approach of credit risk in banking industry. Expert Systems with Applications, 36(4), 8333–8341. https://doi.org/10.1016/j.eswa.2008.10.015.

Liu, C., Ming, Y., Xiao, Y., Zheng, W. & Hsu C-H. (2021). Finding the next interesting loan for investors on a peer-to-peer lending platform. IEEE Access, 9, 111293–111304. https://doi.org/10.1109/ACCESS.2021.3103510.

Liu, Z., Zhang, Z., Yang, H., Wang, G. & Xu Z. (2023). An innovative model fusion algorithm to improve the recall rate of peer-to-peer lending default customers. Intelligent Systems with Applications, 20(March), 200272. https://doi.org/10.1016/j.iswa.2023.200272.

Lyócsa, Š., Vašaničová, P., Misheva, B. H. & Vateha, M. D. (2022). Default or profit scoring credit systems? Evidence from European and US peer-to-peer lending markets. Financial Innovation, 8, (32), 1-21. https://doi.org/10.1186/s40854-022-00338-5.

Ma, Z., Hou, W., & Zhang, D. (2021). A credit risk assessment model of borrowers in P2P lending based on BP neural network. PLoS ONE, 16(8 August), 1–21. https://doi.org/10.1371/journal.pone.0255216.

Munkhdalai, L., Munkhdalai, T., Namsrai, O. E., Lee, J. Y., & Ryu, K. H. (2019). An empirical comparison of machine-learning methods on bank client credit assessments. Sustainability (Switzerland), 11(3), 1–23. https://doi.org/10.3390/su11030699.

Nazareth, N., & Ramana Reddy, Y. V. (2023). Financial applications of machine learning: A literature review. Expert Systems with Applications, 219(January), 119640. https://doi.org/10.1016/j.eswa.2023.119640.

Ribeiro, B., Silva, C., Chen, N., Vieira, A., & Carvalho Das Neves, J. (2012). Enhanced default risk models with SVM+. Expert Systems with Applications, 39(11), 10140–10152. https://doi.org/10.1016/j.eswa.2012.02.142.

Ribeiro-Navarrete, S., Piñeiro-Chousa, J., López-Cabarcos, M. Á., & Palacios-Marqués, D. (2022). Crowdlending: mapping the core literature and research frontiers. Review of Managerial Science, 16(8), 2381–2411. https://doi.org/10.1007/s11846-021-00491-8.

Robisco, A. A. & Martínez, J. M. C. (2022). Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction. Financial Innovation, 8(70), 1-35. https://doi.org/10.1186/s40854-022-00366-1.

Shi, S., Tse, R., Luo, W., D’Addona, S. & Pau, G. (2022). Machine learning-driven credit risk: a systemic review. Neural Computing and Applications, 34(17), 14327–14339. https://doi.org/10.1007/s00521-022-07472-2.

Sigrist, F., & Leuenberger, N. (2023). Machine learning for corporate default risk: Multi-period prediction, frailty correlation, loan portfolios, and tail probabilities. European Journal of Operational Research, 305(3), 1390–1406. https://doi.org/10.1016/j.ejor.2022.06.035.

Song, Y., Wang, Y., Ye, X., Zaretzki, R., & Liu, C. (2023). Loan default prediction using a credit rating-specific and multi-objective ensemble learning scheme. Information Sciences, 629, 599–617. https://doi.org/10.1016/j.ins.2023.02.014.

Sousa, M. R., Gama, J., & Brandão, E. (2016). A new dynamic modeling framework for credit risk assessment. Expert Systems with Applications, 45, 341–351. https://doi.org/10.1016/j.eswa.2015.09.055.

Sun, W., Zhang, X., Li, M., & Wang, Y. (2023). Interpretable high-stakes decision support system for credit default forecasting. Technological Forecasting and Social Change, 196, 122825. https://doi.org/10.1016/j.techfore.2023.122825.

Twala, B. (2010). Multiple classifier application to credit risk assessment. Expert Systems with Applications, 37(4), pp. 3326–3336. https://doi.org/10.1016/j.eswa.2009.10.018.

Valluri, C., Raju, S., & Patil, V. H. (2022). Customer determinants of used auto loan churn: comparing predictive performance using machine learning techniques. Journal of Marketing Analytics, 10(3), 279–296. https://doi.org/10.1057/s41270-021-00135-6.

Wang, C., Zhang, Y., Zhang, W., & Gong, X. (2021). Textual sentiment of comments and collapse of P2P platforms: Evidence from China’s P2P market. Research in International Business and Finance, 58(December 2019), 101448. https://doi.org/10.1016/j.ribaf.2021.101448.

Woo, H., & Sohn, S. Y. (2022). A credit scoring model based on the Myers–Briggs type indicator in online peer-to-peer lending. Financial Innovation, 8(1). https://doi.org/10.1186/s40854-022-00347-4.

Xia, Y., Zhao, J., He, L., Li, Y., & Yang, X. (2021). Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach. International Journal of Forecasting, 37(4), 1590–1613. https://doi.org/10.1016/j.ijforecast.2021.03.002.

Yang, F., Qiao, Y., Qi, Y., Bo J. & Wang X. (2022). BACS: blockchain and AutoML-based technology for efficient credit scoring classification. Annals of Operations Research. https://doi.org/10.1007/s10479-022-04531-8

Yang, M., Lim, M. K., Qu, Y., Li, X., & Ni, D. (2023). Deep neural networks with L1 and L2 regularization for high dimensional corporate credit risk prediction. Expert Systems with Applications, 213(December 2021). https://doi.org/10.1016/j.eswa.2022.118873

Yıldırım, M., Okay, F. Y., & Özdemir, S. (2021). Big data analytics for default prediction using graph theory. Expert Systems with Applications, 176. https://doi.org/10.1016/j.eswa.2021.114840

Zhang, L., Yu, Q., Zhang Y. & Zhou, C. (2023). Adaptive Feature Cross-Compression for Credit Default Prediction. IEEE Access, 11(September), 94322–94334. https://doi.org/10.1109/ACCESS.2023.3309834.

Zhang, X. & Yu, L. (2024). Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods. Expert Systems with Applications, 237, 121484. https://doi.org/10.1016/j.eswa.2023.121484.

Zhu, X., Chu, Q., Song, X., Hu, P. & Peng, L. (2023). Explainable prediction of loan default based on machine learning models. Data Science and Management, 6(3), 123–133. https://doi.org/10.1016/j.dsm.2023.04.003.