Credit Risk Model on Machine learning and prediction Introducing the problem statement First of all, let's try to understand the application that we want to develop or the problem that we are trying to solve. Once we understand the problem statement and it's use case, it will be much easier for us to develop the application. So let's begin!

Here, we want to help financial companies, such as banks, NBFS, lenders, and so on. We will make an algorithm that can predict to whom financial institutes should give loans or credit. Now you may ask what is the significance of this algorithm? Let me explain that in detail. When a financial institute lends money to a customer, they are taking some kind of risk. So, before lending, financial institutes check whether or not the borrower will have enough money in the future to pay back their loan. Based on the customer's current income and expenditure, many financial institutes perform some kind of analysis that helps them decide whether the borrower will be a good customer for that bank or not. This kind of analysis is manual and time-consuming. So, it needs some kind of automation. If we develop an algorithm, that will help financial institutes gauge their customers efficiently and effectively.Your next question may be what is the output of our algorithm? Our algorithm will generate probability. This probability value will indicate the chances of borrowers defaulting. Defaulting means borrowers cannot repay their loan in a certain amount of time. Here, probability indicates the chances of a customer not paying their loan EMI on time, resulting in default. So, a higher probability value indicates that the customer would be a bad or inappropriate borrower (customer) for the financial institution, as they may default in the next 2 years. A lower probability value indicates that the customer will be a good or appropriate borrower (customer) for the financial institution and will not default in the next 2 years.