AI-Driven Financial Risk Assessment and Credit Decision-Making: An Analysis of Predictive Accuracy, Default Risk, and Lending Efficiency.

Studying how artificial intelligence (AI) impacts the financial sector and more specifically credit assessments, is essential for predicting and understanding future trends. This research analyzes advanced risk and credit assessment, focusing on automated systems that enhance credit assessment and optimize portfolio management. Deep learning methods, such as feedforward and recurrent networks, are powerful in predicting financial outcomes and defauls. Defaults are potentially loss-making situations, and early identification allows for mitigation. The data used in this research consists of 1,200 loan applications, and 187 of these applications represent situations where the loans ended in a default situation. Various algorithms, such as logistic regression, decision trees, random forests, and boosted trees, are used to predict default situations. Logistic regression and random forests were the best performers. Income, credit, and debt, as well as loan to value situations, utilization of credit, and prior delinquencies, were the most important variables for predicting default situations. AI increased the capacity and reduced the time needed to review a case for credit, and therefore reduced the time required to process loans. Fairness and interpretability were critical when integrating AI with loan processing. A combination of phasing and flexibility when integrating AI with banking and financial services was suggested.

Artificial intelligence; credit risk; machine learning; default prediction; lending efficiency; financial risk management; credit decision-making

1. Alonso-Robisco, A., & Carbó, J. M. (2022). Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio. International Review of Financial Analysis, 84, 102372. https://doi.org/10.1016/j.irfa.2022.102372 [Google Scholar] [Crossref]

2. Chang, V., Xu, Q. A., Akinloye, S. H., Benson, V., & Hall, K. (2025). Prediction of bank credit worthiness through credit risk analysis: An explainable machine learning study. Annals of Operations Research, 354, 247-271. https://doi.org/10.1007/s10479-024-06134-x [Google Scholar] [Crossref]

3. Financial Stability Board. (2024). The financial stability implications of artificial intelligence. Financial Stability Board. [Google Scholar] [Crossref]

4. Gambacorta, L., Sabatini, F., & Schiaffi, S. (2025). Artificial intelligence and relationship lending (BIS Working Paper No. 1244). Bank for International Settlements. [Google Scholar] [Crossref]

5. Gramegna, A., & Giudici, P. (2021). SHAP and LIME: An evaluation of discriminative power in credit risk. Frontiers in Artificial Intelligence, 4, 752558. https://doi.org/10.3389/frai.2021.752558 [Google Scholar] [Crossref]

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

7. Kozodoi, N., Jacob, J., & Lessmann, S. (2022). Fairness in credit scoring: Assessment, implementation and profit implications. European Journal of Operational Research, 297(3), 1083-1094. https://doi.org/10.1016/j.ejor.2021.06.023 [Google Scholar] [Crossref]

8. Li, J.-P., Mirza, N., Rahat, B., & Xiong, D. (2020). Machine learning and credit ratings prediction in the age of fourth industrial revolution. Technological Forecasting and Social Change, 161, 120309. https://doi.org/10.1016/j.techfore.2020.120309 [Google Scholar] [Crossref]

9. Liu, J., Zhang, S., & Fan, H. (2022). A two-stage hybrid credit risk prediction model based on XGBoost and graph-based deep neural network. Expert Systems with Applications, 195, 116624. https://doi.org/10.1016/j.eswa.2022.116624 [Google Scholar] [Crossref]

10. Liu, Y., Yang, M., Wang, Y., Li, Y., Xiong, T., & Li, A. (2022). Applying machine learning algorithms to predict default probability in the online credit market: Evidence from China. International Review of Financial Analysis, 79, 101971. https://doi.org/10.1016/j.irfa.2021.101971 [Google Scholar] [Crossref]

11. Machado, M. R., & Karray, S. (2022). Assessing credit risk of commercial customers using hybrid machine learning algorithms. Expert Systems with Applications, 200, 116889. https://doi.org/10.1016/j.eswa.2022.116889 [Google Scholar] [Crossref]

12. Montevechi, A. A., Miranda, R. C., Medeiros, A. L., & Montevechi, J. A. B. (2024). Advancing credit risk modelling with machine learning: A comprehensive review of the state-of-the-art. Engineering Applications of Artificial Intelligence, 137, 109082. https://doi.org/10.1016/j.engappai.2024.109082 [Google Scholar] [Crossref]

13. Moscatelli, M., Parlapiano, F., Narizzano, S., & Viggiano, G. (2020). Corporate default forecasting with machine learning. Expert Systems with Applications, 161, 113567. https://doi.org/10.1016/j.eswa.2020.113567 [Google Scholar] [Crossref]

14. Nallakaruppan, M. K., Balusamy, B., Shri, M. L., Malathi, V., & Bhattacharyya, S. (2024). An explainable AI framework for credit evaluation and analysis. Applied Soft Computing, 153, 111307. https://doi.org/10.1016/j.asoc.2024.111307 [Google Scholar] [Crossref]

15. Noriega, J. P., Rivera, L. A., & Herrera, J. A. (2023). Machine learning for credit risk prediction: A systematic literature review. Data, 8(11), 169. https://doi.org/10.3390/data8110169 [Google Scholar] [Crossref]

16. Pérignon, C., & Saurin, S. (2024). The fairness of credit scoring models. Management Science. Advance online publication. https://doi.org/10.1287/mnsc.2022.03888 [Google Scholar] [Crossref]

17. Reserve Bank of India. (2024a). Regulatory principles for management of model risks in credit: Draft circular. Reserve Bank of India. [Google Scholar] [Crossref]

18. Reserve Bank of India. (2024b). Artificial intelligence in financial services and implications for financial stability. RBI Bulletin. Reserve Bank of India. [Google Scholar] [Crossref]

19. Wang, Y., Zhang, Y., Lu, Y., & Yu, X. (2020). A comparative assessment of credit risk model based on machine learning: A case study of bank loan data. Procedia Computer Science, 174, 141-149. https://doi.org/10.1016/j.procs.2020.06.069 [Google Scholar] [Crossref]

• Employee Suggestion Schemes as a Driver of Innovation and Value Creation in Organizations
• Profitability, Growth and Market Valuation of Leading Pharmaceutical Companies in India: A Comparative Financial Analysis
• Problems and Prospects of Sale Employees of Non-Banking Financial Companies
• Core Temperature Estimation and Thermal Management of Lithium-Ion Batteries Using a Nonlinear Adaptive Extended Kalman Filter
• A Conceptual Analysis of How Sustainable Public Procurement Practices Influence Value for Money in Developing Economies