Abstract

Technological advancements have expanded people's needs, increasing loan approval requests in the banking sector. Banks face critical difficulties in surveying credit applications and relieving the dangers related with potential borrower defaults. Assessing every borrower's qualification completely makes this interaction especially troublesome. This examination proposes joining AI (ML) models and group learning ways to deal with foresee the likelihood of tolerating individual advance solicitations. This methodology upgrades the precision of choosing qualified competitors from a pool of candidates, resolving the issues with the credit endorsement process. The proposed strategy benefits advance candidates and bank workers by essentially decreasing the authorizing time. With the financial business' development, more individuals are applying for advances. We utilized four calculations to precisely foresee advance endorsement status: Random forest, Naive Bayes, Decision Tree, and KNN. Among these, the Naive Bayes calculation accomplished the most elevated precision of 83.73%.

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