Suppose you wanted to become a credit risk modeller, working on the parameters used in the Basel framework. That may not be your plan today. But we hope that understanding the job a little better will make you want to find out more.
You would soon discover how much it draws on: statistics, banking regulation, data, and the way credit works. Looking at everything you would need to learn, you might feel that even two years would not be enough to master all these concepts. Where do you begin? We could explain every concept in detail.
But you might end up collecting pieces of knowledge without understanding how they fit together. Knowing how to calculate a probability of default is only a start. You also need to understand what it measures, why a bank needs it, and which decisions it can inform.
So we will begin with the big picture. We will follow the reasoning from a banking question to the data, then to a model and the use of its results. The technical details will find their place as we go.
This first explanation will involve some simplifications. We will not cover every method, every requirement, or every possible situation. But simplifying an explanation should preserve how things work.
We will keep the essential mechanisms and point out the limits of what we explain. The aim is to help you understand how the different parts of the job fit together, so that you know what you are learning and why you are learning it. Becoming a specialist will take more than a few pages.
To understand the job, let us start with one of a bank's most familiar activities: lending money. A bank lends money. But how does it know it will get it back? Imagine a simple situation.
You want to buy an apartment, but you do not have enough money to pay for it. So you go to your bank. To assess your finances, the bank asks you a few questions.
What is your income? What are your monthly expenses? Do you rent or own your home? Do you have a permanent employment contract? After reviewing your application, it agrees to lend you €250,000, repayable over 25 years. You sign a contract committing you to repay the loan, together with interest, according to an agreed schedule. So far, nothing particularly complicated.
But how can the bank know, today, that you will still be able to repay it in five, ten, or twenty years? It cannot. You could lose your job. Your income could fall.
It could also rise. The bank must therefore make a decision today without knowing the events that will affect your ability to repay. It faces uncertainty.
That uncertainty also creates a risk: if you do not repay as agreed, the bank may lose money. This is credit risk. The same applies to businesses.
A company that borrows to build a factory or buy machinery may also run into repayment difficulties. When a borrower becomes unlikely to repay, or has certain material payments overdue, we may be dealing with a default. That is the general idea.
Regulation sets out the criteria to apply: a late payment does not automatically amount to a default. Now imagine thousands of loans. Some borrowers will repay everything.
Others will go through a difficult period. Others will default. The bank expects to incur losses without knowing exactly who will default, when, or how much it will lose.
You may notice that these borrowers are very different: individuals, small businesses, large companies, and governments. It would be difficult to study all of them in the same way. The bank will therefore ask us, as its analysts, to work with more comparable groups.
We might study a portfolio of residential mortgages or a portfolio of loans to large companies.
Source: Towards Data Science · Summarized by HeadlinesBriefing