The banking industry contributes significantly to the global economic development of any country. The quantity of credit that has been granted to the public is one of the primary sources of a bank's income. Credit gains account for the majority of the bank's financial gains. Now, it's important for the banks to make a sound decision when determining who should be given credit. The personnel are unsure of whether the accepted consumer would return the credit even after the long manual vetting of credit applicants. We are attempting to lessen the vulnerability behind the authorised person in order to lessen the stress on the bank staff and to save their resources. To do this, a dataset from the Kaggle library that includes the historical data of credit applicants who were approved for credit may be used. Our main objective is to determine if a consumer is eligible for a loan or not by predicting their eligibility. We have tested a broad range of machine learning techniques. To minimise staff effort, which results in a computation error while trying to locate a candidate for credit authentication. Here, we look at the different information about the customers, with features like income, past credit history, educational status, and their asset information from previous records of credit applicants regarding their loan approval, and the best elements are chosen which have a clear impact on the outcome for our credit authentication system.