Risk analytics plays a critical role in the banking industry by minimizing the loss of money through lending it to the customers who can default. The banks always fear about giving loans to the individuals in need of money because of the wrong history or no credit history of the individuals. Several individuals take the advantage of bank loans and default. Due to the fear of credit risk, many banks are employing risk analytics to perform a clear evaluation before lending a loan.
Who Performs Risk Analysis for a Bank?
Generally, every bank employs Credit Risk Analyst either within the bank or in an outside agency. The major job of the analyst is to evaluate the loan applications received for the bank and then determine the loan repayment capability of the individuals based on various key factors including the past repayment history of the individual. The analysis employs various analytics techniques to study the risk of lending to a customer and the business risk of not lending to a customer. Based on clear assessment, the analysts presents their findings to the bank.
What Data Does Banks Use for Risk Analysis?
The banks can use data from various sources for risk analysis. The sources generally include internal and external. The data includes both transactional data and behavioral data. The banks are combining data collected from multiple sources. Earlier, the banks used to rely majorly on the credit reports from sources like CIBIL and market information. But, the banks are now gathering data from multiple sources like public information, customer transactions from utility spending and market purchases. The data from customer rating websites and social media is also being used. The banks are integrating all this data to get valuable insights about a customer or an institution seeking loan.
Banks Getting Help from Advancements in Analytics
Various latest analytical techniques are emerging in the risk analytics domain. Also, the computing power that can evaluate huge amounts of data is available now. With the help of these, the banks can perform a deeper analysis and get valuable insights from the data. Also, due to automation of many processes involved in the risk analysis, the process has become faster now a days.
With the advancements in computing power availability, the banks are now affording to deploy technologies at a higher scale to perform risk analysis. Machine learning and artificial intelligence are being widely used. The new tools based on these technologies improve the decision making capabilities. Also, the data that is built from latest techniques like natural-language processing and geospatial analysis improve the accuracy levels. The steps that used to be manual like data collection and cleaning are automated with these technologies. This improves the speed at which analysis can be done and reports be generated. Overall, the risk analysts can now assess risk accurately and at a high speed.
Application of Exploratory Data Analysis (EDA)
The banks can use EDA to study the large volumes of consumer data and assess the loan repayment capability of the individuals. The individuals who fail in the analysis can be rejected from getting a loan.
There are two types of risks associated with loan processing. When an individual has the capability to pay a loan, the rejecting the application means a loss to the bank. When an individual does not have sufficient repayment capacity, then giving loan to such individual also means a loss to the bank. EDA helps to solve this problem with detailed and thorough analysis done.