| dc.description.abstract | Banks' profitability is highly dependent on loans, which constitute a substantial amount of
their income. However, the task of precisely selecting real persons who would repay their
loans becomes challenging due to the high volume of loan applicants. The bank's revenue
and profitability are directly affected by the choice to accept or reject a loan application.
The manual review of loan applications is susceptible to misconceptions and inaccuracies,
resulting in the approval of applicants who may not truly be creditworthy. The aim of this
study is to develop a loan prediction system employing machine learning approaches to
address the issue. The system will autonomously look over and choose suitable applicants
for loans, thus reducing the need on manual processing. The suggested framework uses the
Decision Tree method, an algorithm for machine learning that can make judgments based
on input data. The Decision Tree algorithm analyzes past loan data, considering into
account numerous factors such as income, credit score, employment history, and loan
amount. It utilizes this data to generate a prognostic model that evaluates the probability of
loan reimbursement. By utilizing historical loan data and information from past loan
applicants, the model has the ability to forecast whether a loan application should be
approved or denied. Using such a system offers benefits to both employees at banks and
those applying for loans. Automating the selection process greatly decreases the time
needed for loan approval, allowing applicants to access funds more quickly. In addition,
the technique enhances the accuracy of applicant screening, diminishing the likelihood of
providing loans to those who have a higher probability of defaulting. The project efforts to
make use of machine learning approaches, specifically the Decision Tree classifier, in order
to forecast loan outcomes and improve the loan approval process. This automated method
helps tackle the difficulties linked to manual processing and enhances the efficiency and
precision of selecting loan applicants. In conclusion, this project effectively created a
highly precise and efficient Decision Tree model that can greatly enhance the loan approval
process for banks. It achieves this by automating predictions, minimizing manual work,
and delivering dependable loan approval or rejection outcomes that align with the
requirements of the banking sector | en_US |
