Credit Risk Management in the Banking Sector: APredictive Analytics Approach Using StructuredFinancial Data
Keywords:
Credit risk, Predictive analytics, Structured financialdata, Loan Default, Banking SectorAbstract
Credit risk management is a vital function in the banking sector, directlyinfluencing financial stability and lending decisions. With the growingavailability of structured financial data, there is an increasing need fordata-driven approaches to effectively assess and manage credit risk. Thisstudy examines key factors influencing credit risk and exploresrelationships between financial variables and loan default using structuredfinancial data. A quantitative research design based on secondary dataanalysis was adopted. The dataset included demographic, financial, loan-related, and credit history variables. Descriptive statistics, correlation, andcomparative analyses were applied to identify patterns associated withloan default. Findings reveal that loan-to-income ratio, interest rate, andloan amount are positively associated with default risk, while income andemployment length are negatively related. Default borrowers exhibithigher financial burden and lower repayment capacity. Categorical factorssuch as loan grade and prior default history also significantly differentiaterisk levels. The study highlights the importance of structured financialdata in enhancing credit risk assessment. By focusing on key riskindicators and adopting data-driven strategies, financial institutions canimprove decision-making and lending practices, contributing to moreeffective credit risk management.
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