LMRPID-397479
Page 31
30th July 2024
The Impact of Artificial Intelligence on Credit Risk Assessment and Loan Decision-Making in Commercial Banks: Challenges and Opportunities
Author: FARZANA AKSAR SOHELY | LGMID: 27199320190101959
Reviewed by:
Lilac School of Business (LSB)
Paper preview
1. Abstract
2. Introduction
3. Literature Review
4. Methodology
5. Findings
6. Conclusion
7. References
Abstract
Lending remains a cornerstone of the banking and financial services industry, directly impacting both economic stability and individual financial growth. With the rise of digital transformation, commercial banks increasingly integrate Artificial Intelligence (AI) into credit risk assessment and loan decision-making processes to enhance operational efficiency, reduce default risks, and expand access to financial services. Traditional credit scoring models, such as the FICO score, heavily rely on historical financial data and fixed parameters, often excluding individuals with limited or no credit history. These outdated systems are being challenged by the growing complexity of financial ecosystems and the need for real-time, inclusive decision-making. AI-powered models offer a significant leap forward by utilizing big data analytics, machine learning algorithms, and predictive modeling to assess borrower creditworthiness with greater accuracy and speed. This research aims to evaluate the transformative impact of AI on credit and loan operations in commercial banks, using qualitative methods that synthesize insights from academic literature, industry case studies, and global reports. It identifies key benefits, including dynamic risk assessment, automated credit qualification, fraud detection, faster loan approvals, and improved financial inclusion for underserved communities. However, integrating AI also presents serious challenges, such as data privacy risks, algorithmic bias, lack of transparency, regulatory compliance concerns, and issues around explainability and accountability. The findings suggest that while AI holds immense promise in reshaping lending systems, its implementation must be approached with ethical foresight and robust governance. Financial institutions, regulators, and developers must collaborate to develop fair, transparent, and inclusive AI frameworks. This study contributes a comprehensive perspective to the discourse on AI-driven financial transformation and serves as a guide for building intelligent, responsible, and inclusive credit systems in the era of digital finance.
References
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Keywords
Artificial Intelligence, Credit Risk Assessment, Loan Decision-Making, Commercial Banks, Financial Inclusion,
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