The Problem of Credit Risk as the Main Task of the Bank
Banks offer many services but most of them are related to credit, for example, business loans, checking accounts, payment services, cash management. Banks act as brokers between supply and demand of securities, and they transform short-term deposits into medium- and long-term credits. One of the basic tasks which any finance institution must deal with, in the current competitive and turbulent business environment, is to minimize its credit risk.
Credit scoring is built in the 1960s, but widely credit scoring popularity is being increased in the 1975s when credit cards business area had become popular. Credit scoring is a tool which is used to evaluate the risk of credit applications. Credit scoring is one of the most interesting and important area for research all around the world. Credit scoring is a credit risk management technique that analyzes the borrower’s risk and assists organizations to decide whether or not to grant credit to consumers. A common approach of credit scoring is to apply a classification technique on data of previous customers (both good credit customers and bad credit customers) in order to find a relationship between the customers characteristics and potential failure to service their debt. Most of the institutions use credit scoring techniques (utilizing information from the consumers past credit history and current economic conditions) to determine which applicants will pay back their liabilities.
In a credit there is five Cs which is a used by lenders to gauge the credit worthiness of potential borrowers. The system weighs five characteristics of the borrower and conditions of the loan, attempting to estimate the chance of default and, consequently, the risk of a financial loss for the lender. The five key criteria Cs of credit are character, capacity, capital, conditions and collateral.
Credit risk refers to the probability of loss due to a borrower’s failure to make payments on time at any type of debt. Credit risk is the practice of mitigating losses by understanding the adequacy of a bank’s capital and loan loss reserves at any given time a process that has long been a challenge for financial institutions. For that, Scoring methods traditionally estimate the creditworthiness of a credit applicant or a current customer. They predict the probability that an applicant or existing borrower will default or become delinquent.
Overall, different techniques such as discriminant analysis, probit analysis, logistic regression, linear programming, decision trees, neural networks and genetic algorithms have been offered to build the credit scoring problems. Two scientific fields are encountering here: machine learning and statistics. While the scope and methods of these fields are generally different, drawing a clear differentiating line between them in the context of the classification is nearly impossible. For example, the logistic regression is used for classification and is considered to be a statistical tool. On the other hand, the neural networks and others are usually referred to as tools from the field of machine learning.
