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LOAN FINANCIAL RISK ANALYSIS AND VISUALISATION
ISSN: 2582 - 9130
Publisher: author
LOAN FINANCIAL RISK ANALYSIS AND VISUALISATION
Indexed in
Technology and Engineering
ARTICLE-FACTOR
1.3
Article Basics Score: 2
Article Transparency Score: 3
Article Operation Score: 2
Article Articles Score: 3
Article Accessibility Score: 3
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International Category Code (ICC):
ICC-1802
Publisher: Krishma Publication
International Journal Address (IAA):
IAA.ZONE/2582384269130
eISSN
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2582 - 9130
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Abstract
Credit risk is associated with the possibility of a client failing to meet contractual obligations, such as mortgages, credit card debts, and other types of loans. Minimising the risk of default is a major concern for financial institutions. For this reason, commercial and investment banks, venture capital funds, asset management companies and insurance firms, to name a few, are increasingly relying on technology to predict which clients are more prone to stop honouring their debts. By this project, we are trying to compare three gradient boosting ensemble machine learning algorithms and find which one can work better based on some performance metrics like Precision, Recall and F1 score. After successfully comparing the three algorithms we can draw our decisions by communicating with the officials with which algorithm they wanted to proceed with. People who are exploring machine learning as a field to shift their careers or people who...
