Credit Risk Management and Loan Approval Decisions in Banking Institutions: A Governance-Based Perspective
Keywords:
Credit Risk Management, Loan Approval Decisions, Financial Governance, Banking Institutions, Machine Learning, Credit ScoringAbstract
This research seeks to explore the impact of credit risk factors on credit approval decisions in financial institutions, and particularly to understand these decisions from a governance perspective. This research adopts a quantitative approach, analysing a secondary dataset sourced from Kaggle, consisting of 5,000 individual borrowers' data. The analysis is performed using Python, including descriptive statistics, correlation, logistic regression and machine learning (random forest). The findings show that credit score and income are the most important factors in loan approval, with credit score being the most influential. The significant skewness in approved and rejected loans indicates a risk-averse lending policy. In addition, machine learning approaches have higher predictive accuracy than classical statistical techniques. The research draws attention to loan approval systems as governance structures that implement risk management strategies in banks. The research highlights the need for incorporating sophisticated analytics in credit assessment. This research adds to the body of knowledge by connecting credit risk management to governance frameworks and provides a unique insight into how financial institutions govern credit lending.
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