We have been using this term credit risk since the start of this chapter and many of you might be wondering what exactly does this mean, even though you might have guessed it after reading the previous section. Here, we will be explaining this term clearly so that you will have no problem in understanding the data and its features in the subsequent sections when we will be analyzing the data.

The standard definition of credit risk is the risk of defaulting on a debt which takes place due to the borrower failing to make the required debt payments in time. This risk is taken by the lender since the lender incurs losses of both the principal amount as well as the interest on it.

In our case, we will be dealing with a bank which acts as the financial organization giving out loans to customers who apply for them. Hence, customers who might default on the loan payment would be credit risks for the bank. By analyzing customer data and applying machine learning algorithms on it, the bank will be able to predict in advance which customers might be potential credit risks. This will help in risk mitigation and in minimizing losses by not giving away loans to customers who could be credit risks for the bank.