• NIKHIL MADANE et al.


With the improvement in the banking sector, lots of people apply for bank loans, but the bank has its limited slots that it only has to give and sell to limited people, so find out who can be given the loan that will be a safer option for the banks. So we're trying to reduce this risk factor behind choosing the safe individual in this paper to save lots of effort and resources from the bank. This paper proposes a loan approval system based on certain attributes to decide whether or not a loan should be granted to an individual. In this paper, the model we are proposing for the bankers would help them predict the trustworthy persons who have applied for a loan, thus increasing the chances of retaining their loans in time. This analysis is created using the algorithm of the Decision tree to estimate a loan's future. We will review the credit scoring for mortgage loans and the conditions that contribute to the rejection of the borrower. This will be that applicant's review and evaluate the percentage of applications that have been accepted but should have been rejected. The risk of mortgage loans demands a great detail of each applicant's review and walking the fine line of who should and should not be approved.