Python Tutorial : Understanding credit risk

Want to learn more? Take the full course at https://learn.datacamp.com/courses/credit-risk-modeling-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Hi, my name is Michael Crabtree and I am a data scientist at Ford Motor Company. I will show you some concepts and techniques for credit risk modeling using Python. What exactly is credit risk? Credit risk is the risk that someone who has borrowed money will not repay it all. Think of this risk as the difference between lending money to a person and purchasing a government bond. With government bonds, it's almost guaranteed to be paid back, but not when lending money to people. A loan is in default when the lending agency is reasonably certain the loan will not be repaid. We will use machine learning models to determine this. Consider this example: we've loaned 300 dollars to someone who has made two payments but not the final payment. It is at this point we consider the loan to be in default. Predicting this beforehand is useful for us to estimate expected loss. The expected loss is the amount that the firm loses as a result of the default on a loan. The expected loss is a simple calculation of the following three components. The probability of default, which is the likelihood someone will default on a loan. The exposure at default which is the amount outstanding at the time of default. And the loss given default which is the ratio of the exposure against any recovery from the loss. From our example, the 100 dollars we were owed is our exposure, and if we sell that debt for 20 dollars, our loss given default would be 80 percent. The formula for expected loss is the probability of default times the exposure at default and loss given default. This course will focus on the probability of default. For modeling probability of default we generally have two primary types of data available. The first is application data, which is data tha