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Influence of Artificial Intelligence on Credit Risk Assessment in Banking Sector

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  • Michael Brown

Abstract

Purpose: The aim of the study was to examine the influence of artificial intelligence on credit risk assessment in banking sector. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that AI-driven models demonstrate superior performance in identifying risky borrowers and capturing complex credit risk patterns compared to traditional methods. Additionally, the integration of explainable AI (XAI) techniques has enhanced transparency and interpretability in credit risk assessment processes, facilitating better understanding among stakeholders and improving decision-making transparency. Unique Contribution to Theory, Practice and Policy: Decision theory & technology acceptance model (TAM) may be used to anchor future studies on influence of artificial intelligence on credit risk assessment in banking sector. Continuously invest in research and development to advance the theoretical understanding of AI-driven credit risk assessment models. This includes exploring the integration of machine learning with behavioral economics theories to better predict borrower behavior and default probabilities. Encourage banks to adopt a hybrid approach that combines the strengths of AI-driven models with human expertise. Develop comprehensive regulatory guidelines and standards to govern the use of AI in credit risk assessment and ensure ethical and responsible practices. This includes establishing transparent model validation and governance frameworks to mitigate the risks of algorithmic bias, data privacy violations, and discriminatory lending practices. Regulatory authorities should also promote industry-wide collaboration and knowledge sharing to foster innovation while safeguarding consumer interests and financial stability.

Suggested Citation

  • Michael Brown, 2024. "Influence of Artificial Intelligence on Credit Risk Assessment in Banking Sector," International Journal of Modern Risk Management, IPR Journals and Book Publishers, vol. 2(1), pages 24-33.
    • Handle: RePEc:bdu:oijmrm:v:2:y:2024:i:1:p:24-33:id:2641
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    1. David J. Hand, 2018. "Statistical challenges of administrative and transaction data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 555-605, June.
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