AI-Based Credit Scoring Models for Smart Banking

  • Authors

    • Prof. Liam O’Connor Faculty of Science and Technology, Durham College, Whitby, Canada. Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2023PI6RS29

    Published 02-03-2023

  • Credit Scoring, Artificial Intelligence, Machine Learning, Smart Banking, Risk Assessment, Explainable Ai, Financial Technology

    Issue

    Vol. 6 No. 1 (2023)

    Section

    Articles

    How to Cite

    [1]
    L. O’Connor, “AI-Based Credit Scoring Models for Smart Banking”, IJAIDT, vol. 6, no. 1, pp. 01–13, Feb. 2023, doi: 10.67228/30713315/IJAIDT-2023PI6RS29.

  • Abstract

    Credit scoring is vital in modern banking for loan decisions, risk management, and financial inclusion. Traditional models rely on limited, static financial data and struggle to adapt to changing conditions. This paper proposes an AI-based credit scoring approach that uses machine learning, deep learning, and hybrid models to improve accuracy and scalability. The framework integrates financial data with alternative data such as transaction behavior, digital footprints, and repayment patterns. Techniques like decision trees, random forests, SVMs, gradient boosting, and neural networks are analyzed and compared with traditional methods. It also emphasizes explainable AI (XAI) to ensure transparency, fairness, and regulatory compliance. Results show that AI models outperform conventional approaches in accuracy and risk prediction. The study highlights the importance of interpretability, bias reduction, and strong governance, concluding that AI-driven credit scoring enhances smart banking, customer experience, and financial inclusion.

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