Credit Risk Modelling
Authors
Ashok Bhavikatti, Roma
Issue Date
2023-08
Degree
Msc in Data Analytics
Publisher
Dublin Business School
Rights holder
Rights
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Abstract
Through a series of loan evaluations, a credit risk modeling method evaluated the dependability of the borrower. High-risk candidates were initially weeded out using basic borrower data and credit scores. For a more accurate risk assessment, detailed financial data including income, assets, and liabilities were subsequently examined. The algorithm continuously included new variables, improving accuracy and offering a sophisticated default probability evaluation. This strategy offered a solid framework for anticipating credit problems by combining personal behavior and financial information. These models proved essential for lenders, enabling informed lending decisions and reducing possible losses due to defaults.