Financial Institutions depend on credit risk scores to make a diverse lending decision that includes unsecured credit, for example, loan approval, interest charges, repayment tenure, and others. There are various challenges that this industry faces with credit scoring. To overcome these and to identify new opportunities, machine learning models are used to analyze the repayment ability of the user and prospective defaulters.
Leading financial bodies to carry out an extensive process of Credit report and scoring based on the data points. The total intensity of debt, number of open accounts, types of loans taken in the past, repayment of debts, amount of credit available, credit utilizations, exceptional debt collection, and other public records including tax liens, bankruptcy, foreclosure, etc., come under data points.
It is a tough task to get data points for small businesses that provide loans. Processing unsecured loans for prospects such as students become a tricky task. Such cases include other steps like confirmation of a user’s identity, address, user’s income, education details, and Credit history of the user if available. Right after the groundwork of risk assessment, the request sets off through regulatory, technological, and operational analysis to decide on the loan approval, interest rate, or repayment tenure.
The next step is clustering that they use tools such as KNIME and DBScan to distinguish the first time users and repeat users. Next is one of the most significant steps in determining the probability of a user being a good or defaulter client. This one is named as feature engineering and selection that uses logistic regression and other such techniques. Finally, to find the credit score of users and to determine the probability of being a defaulter, they use techniques such as deep learning, machine learning, random forest, AI, neural networks, and GBM.
Further, they analyze data from social media, location information, online shopping history, travel data, utility bills, in addition to the phone and mobile data such as contacts and SMS. These data points are analyzed to distinguish if the person requesting the loan is genuine and is using a genuine and original account or phone. However, this information is accessed only after having the user’s consent.