Credit Risk Analysis of Loans using Social media Information

The core business of the banking sector is sanctioning loans to different individuals and industries. The credit risk analysis of these elements gives guarantee about regular repayment of loan. As a result, healthy business firms repay their loan regularly thereby increasing good return on investment to bank. It is possible to increase the accuracy of credit risk calculation using current technology like Big Data and different analytical tools. In our approach, along with traditional parameters like profit/loss, financial history, financial status of directors, cash flow, we also included non-formatted data like news and informal information for analysis. This information can be included as positive, negative and regular. This information can be collected using Big Data techniques from websites, news websites, government agencies and external agencies. This is used to construct the credit scoring models and to predict the borrower’s creditworthiness and default risk. Looking at the uncertainty associated with judging the credit of borrower, it is necessary to add new tools and methods to get maximum correctness. Our approach to use Big Data analysis tools to input informal sources available on internet, will increase the accuracy of finding good borrower for banks