An attempt has been made to develop an algorithm for banks to check the credibility of borrowers to avoid nonperformance assets. People move towards different banks for loan purpose to fulfil their financial needs. Approaching bank for loan is increasing day by day mainly for child marriage, education, agriculture, business, home loan etc. Some people take the loan and they won’t pay back in time or some will move out of the country without any intimation, so that bank will go in loss. Even now in covid-19 pandemic many industries were closed but they need to give salary to the employees, need to pay rent and electricity bills too for that they will approach bank for loan. For all these cases bank first need to analyse their Credit Information Bureau India Limited score and check whether they had done loan repayments in appropriate time or not. In the present work the effectiveness of K nearest neighbor algorithm were analysed. This research were carried out using python. The accuracy of this classifier is analysed using following metrics such as Jaccard index, F1-score and LogLoss. This helps to find the potential of the customer which is much higher than the data mining classification algorithm and thus it helps in sanctioning loans.
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