Intelligent Prediction of Loan Eligibility using Soft Computing Towards Digital Banking Sector

Machine learning algorithms can be used in variety of fields for the prediction and decision making. Banking sector has vast scope where machine learning algorithm can be implemented and predict better solutions. Loan is very important term which plays role in all financial position of general public. In order to satisfy additional needs which cannot be afforded within income of a person, credit money can be taken from bank and other financial institutions against the agreement of paying additional money to be returned in the form of interest. But while providing loan to any person bank should check eligibility of the person in order to assure that loan can be repaid by person well within time. Bank should check eligibility in order to obtain security of the amount paid. Loan eligibility gets checked against different criteria and bank comes up with the decision with regards to loan payment. Certainly, the person who does not meet defined criteria will not be paid with the loan amount and gets rejected by the bank. The person who fits all predefined conditions checklist of the bank, can be paid with the approved loan amount. The decision to be made by bank only and all rights reserved with the bank. Sometimes checking eligibility of the customer becomes tricky and time consuming while it needs separate efforts to be taken by bank executives to come to decision to approve or deny the loan amount. While predefined eligibility includes monthly income, real assets in hand, previous loan history and number of other important criteria, credit card information of the person plays important role based on the history of credit card payment. Cybil score gets calculated based on the transaction and repayment of the credit card bill. Implemented algorithm uses all credit card information and calculates person’s eligibility towards loan. Machine learning algorithm has been implemented on the gathered data to obtain results. Data collected from various sources of banking sector and data pre-processing has been done to filter the data in order to remove unwanted facts and figures. Hence training set has been obtained on which machine learning algorithm has been applied to come up with the model which helps to elect on the loan payment decision.