AI-Enabled Advanced Decision Intelligence Framework for Optimizing Credit Risk Management in Saudi Enterprises

References

[1] X. Dastile, T. Celik, and M. Potsane, “Statistical and machine learning models in credit scoring: A systematic literature survey,” Applied Soft Computing, vol. 91, Art. no. 106263, 2020, doi: 10.1016/j.asoc.2020.106263. Crossref

[2] N. Bussmann, P. Giudici, D. Marinelli, and J. Papenbrock, “Explainable machine learning in credit risk management,” Computational Economics, vol. 57, pp. 203–216, 2021, doi: 10.1007/s10614-020-10042-0. Crossref

[3] S. Sachan, J.-B. Yang, D.-L. Xu, D. E. Benavides, and Y. Li, “An explainable AI decision-support-system to automate loan underwriting,” Expert Systems with Applications, vol. 144, Art. no. 113100, 2020, doi: 10.1016/j.eswa.2019.113100. Crossref

[4] V. B. Djeundje, J. Crook, R. Calabrese, and M. Hamid, “Enhancing credit scoring with alternative data,” Expert Systems with Applications, vol. 163, Art. no. 113766, 2021, doi: 10.1016/j.eswa.2020.113766. Crossref

[5] V. Moscato, A. Picariello, and G. Sperli, “A benchmark of machine learning approaches for credit score prediction,” Expert Systems with Applications, vol. 165, Art. no. 113986, 2021, doi: 10.1016/j.eswa.2020.113986. Crossref

[6] B. R. Gunnarsson, S. vanden Broucke, B. Baesens, M. Oskarsdottir, and W. Lemahieu, “Deep learning for credit scoring: Do or don't?,” European Journal of Operational Research, vol. 295, no. 1, pp. 292–305, 2021, doi: 10.1016/j.ejor.2021.03.006. Crossref

[7] E. Dumitrescu, S. Hue, C. Hurlin, and S. Tokpavi, “Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects,” European Journal of Operational Research, vol. 297, no. 3, pp. 1178–1192, 2022, doi: 10.1016/j.ejor.2021.06.053. Crossref

[8] J. A. Bastos and S. M. Matos, “Explainable models of credit losses,” European Journal of Operational Research, vol. 301, no. 1, pp. 386–394, 2022, doi: 10.1016/j.ejor.2021.11.009. Crossref

[9] A. C. Bueff, M. Cytrynski, R. Calabrese, M. Jones, J. Roberts, J. Moore, and I. Brown, “Machine learning interpretability for a stress scenario generation in credit scoring based on counterfactuals,” Expert Systems with Applications, vol. 202, Art. no. 117271, 2022, doi: 10.1016/j.eswa.2022.117271. Crossref

[10] G. Babaei, P. Giudici, and E. Raffinetti, “Explainable FinTech lending,” Journal of Economics and Business, vols. 125–126, Art. no. 106126, 2023, doi: 10.1016/j.jeconbus.2023.106126. Crossref

[11] Y. Chen, R. Calabrese, and B. Martin-Barragan, “Interpretable machine learning for imbalanced credit scoring datasets,” European Journal of Operational Research, vol. 312, no. 1, pp. 357–372, 2024, doi: 10.1016/j.ejor.2023.06.036. Crossref

[12] H. Mohammadnejad-Daryani, A. A. Taleizadeh, and D. Pamucar, “A novel profit-driven framework for model evaluation in credit scoring,” Engineering Applications of Artificial Intelligence, vol. 137, Art. no. 109137, 2024, doi: 10.1016/j.engappai.2024.109137. Crossref

[13] S. Shi, R. Tse, W. Luo, S. D'Addona, and G. Pau, “Machine learning-driven credit risk: A systemic review,” Neural Computing and Applications, vol. 34, pp. 14327–14339, 2022, doi: 10.1007/s00521-022-07472-2. Crossref

[14] G. F. Bone-Winkel and F. Reichenbach, “Improving credit risk assessment in P2P lending with explainable machine learning survival analysis,” Digital Finance, vol. 6, pp. 501–542, 2024, doi: 10.1007/s42521-024-00114-3. Crossref

[15] D. Chen, J. Ye, and W. Ye, “Interpretable selective learning in credit risk,” Research in International Business and Finance, vol. 65, Art. no. 101940, 2023, doi: 10.1016/j.ribaf.2023.101940. Crossref

[16] N. Kozodoi, J. Jacob, and S. Lessmann, “Fairness in credit scoring: Assessment, implementation and profit implications,” European Journal of Operational Research, vol. 297, no. 3, pp. 1083–1094, 2022, doi: 10.1016/j.ejor.2021.06.023. Crossref

[17] P. Giudici, M. Centurelli, and S. Turchetta, “Artificial Intelligence risk measurement,” Expert Systems with Applications, vol. 235, Art. no. 121220, 2024, doi: 10.1016/j.eswa.2023.121220. Crossref

[18] A. A. H. A. Khatir and M. Bee, “Machine learning models and data-balancing techniques for credit scoring: What is the best combination?,” Risks, vol. 10, no. 9, Art. no. 169, 2022, doi: 10.3390/risks10090169. MDPI

[19] V. Chang, Q. A. Xu, S. H. Akinloye, V. Benson, and K. Hall, “Prediction of bank credit worthiness through credit risk analysis: An explainable machine learning study,” Annals of Operations Research, vol. 354, pp. 247–271, 2025, doi: 10.1007/s10479-024-06134-x. Crossref

[20] M. K. Nallakaruppan, B. Balasubramanian, B. Balusamy, M. L. Shri, V. Malathi, M. Veeraraghavan, and S. Bhattacharyya, “An Explainable AI framework for credit evaluation and analysis,” Applied Soft Computing, vol. 153, Art. no. 111307, 2024, doi: 10.1016/j.asoc.2024.111307. Crossref

[21] D. Ahelegbey, P. Giudici, and V. Pediroda, “A network based fintech inclusion platform,” Socio-Economic Planning Sciences, vol. 87, Art. no. 101555, 2023, doi: 10.1016/j.seps.2023.101555. ScienceDirect

[22] D. Tripathi, D. R. Edla, V. Kuppili, and A. Bablani, “Evolutionary Extreme Learning Machine with novel activation function for credit scoring,” Engineering Applications of Artificial Intelligence, vol. 96, Art. no. 103980, 2020, doi: 10.1016/j.engappai.2020.103980. Crossref

[23] W. Liu, H. Fan, and M. Xia, “Step-wise multi-grained augmented gradient boosting decision trees for credit scoring,” Engineering Applications of Artificial Intelligence, vol. 97, Art. no. 104036, 2021, doi: 10.1016/j.engappai.2020.104036. Crossref

[24] W. Zhang, D. Yang, S. Zhang, J. H. Ablanedo-Rosas, X. Wu, and Y. Lou, “A novel multi-stage ensemble model with enhanced outlier adaptation for credit scoring,” Expert Systems with Applications, vol. 165, Art. no. 113872, 2021, doi: 10.1016/j.eswa.2020.113872. Crossref

[25] C.-F. Wu, S.-C. Huang, C.-C. Chiou, and Y.-M. Wang, “A predictive intelligence system of credit scoring based on deep multiple kernel learning,” Applied Soft Computing, vol. 111, Art. no. 107668, 2021, doi: 10.1016/j.asoc.2021.107668. Crossref

[26] V. Chang, S. Sivakulasingam, H. Wang, S. T. Wong, M. A. Ganatra, and J. Luo, “Credit risk prediction using machine learning and deep learning: A study on credit card customers,” Risks, vol. 12, no. 11, Art. no. 174, 2024, doi: 10.3390/risks12110174. MDPI

[27] N. Suhadolnik, J. Ueyama, and S. Da Silva, “Machine learning for enhanced credit risk assessment: An empirical approach,” Journal of Risk and Financial Management, vol. 16, no. 12, Art. no. 496, 2023, doi: 10.3390/jrfm16120496. MDPI

[28] H. H. Al-Baity, “The Artificial Intelligence revolution in digital finance in Saudi Arabia: A comprehensive review and proposed framework,” Sustainability, vol. 15, no. 18, Art. no. 13725, 2023, doi: 10.3390/su151813725. MDPI

[29] A. Eskandarany, “Adoption of artificial intelligence and machine learning in banking systems: A qualitative survey of board of directors,” Frontiers in Artificial Intelligence, vol. 7, Art. no. 1440051, 2024, doi: 10.3389/frai.2024.1440051. Frontiers

[30] H. H. Khan, S. Khan, and A. Ghafoor, “Fintech adoption, the regulatory environment and bank stability: An empirical investigation from GCC economies,” Borsa Istanbul Review, vol. 23, no. 6, pp. 1263–1281, 2023, doi: 10.1016/j.bir.2023.10.010. ScienceDirect

@article{1723458,
      author = {Puthan Veettil Abdulla Mohd Kayoom},
      title = {AI-Enabled Advanced Decision Intelligence Framework for Optimizing Credit Risk Management in Saudi Enterprises},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {3},
      pages = {2962-2974},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1723458.pdf},
      abstract = {Artificial intelligence is reshaping credit risk management by enabling lenders to process high-dimensional financial, transactional and behavioral information at a speed and granularity that conventional scorecards cannot easily match. Yet stronger prediction alone does not constitute decision intelligence. Credit decisions are economically consequential, regulated, path-dependent and exposed to model risk, data drift, unfairness, cybersecurity threats and weak organizational accountability. This review develops an AI-enabled advanced decision intelligence framework for optimizing credit risk management in Saudi enterprises. Using a structured integrative review of peer-reviewed literature, the paper synthesizes evidence on machine-learning credit scoring, explainable artificial intelligence, alternative data, class imbalance, profit-sensitive evaluation, model governance and Saudi digital-finance conditions. The synthesis indicates that ensemble and nonlinear models often improve discriminatory power, but their value depends on calibrated probabilities, stable explanations, portfolio-level economics, rigorous validation and human oversight. The proposed framework therefore links enterprise data, predictive analytics, explainability, risk appetite, workflow orchestration and continuous monitoring rather than treating credit scoring as an isolated classification task. Particular attention is given to Saudi requirements for trustworthy data use, Shariah-sensitive product structures, financial-sector digitalization and Vision 2030 objectives. The review concludes that Saudi enterprises should adopt a governed decision-intelligence architecture in which AI augments rather than replaces accountable credit judgment, and where performance is evaluated through accuracy, fairness, interpretability, robustness and economic outcomes simultaneously.},
      keywords = {artificial intelligence; credit risk; decision intelligence; explainable AI; Saudi Arabia; enterprise risk management; fintech; model governance},
      month = {September},
      doi = {https://doi.org/10.64388/IREV10I3-1723458}
  }