In this video, we want to discuss the concept of credit and how it winds up being priced. From previous videos, we have talked quite a bit about how people make payments between one another. The ideas of credit and payment are essentially inseparable. When we make a payment to someone, it is a means to simplify the transactions of goods or services. So rather than directly exchanging those goods or services, we use some sort of a currency in order to make that transaction. The use of gold, cash, check, credit or Bitcoin is an extension of credit. Essentially, what we are doing is we are trusting that we will be able to get the goods or services that we desire by using this gold, cash, check, credit card or Bitcoin. The word credit, in fact, comes from ancient proto-Indo-European language kred d'eh, to place one's heart. And so as one can see from the definition, the idea of extending credit is intricately related to trust, the promise of payment. The idea of credit perhaps dates back as long as people were exchanging goods with one another. But we know for a fact that it at least dates back to ancient Sumer, where we have found artifacts that indicate that people were making loans to one another. What I want you to do now is take just a few moments and think about the question, if someone were to ask you for a loan, what kind of things you would consider? Imagine that a friend or a co-worker were to ask you for money for something, what would help make the decision as to whether you would give that to them or not? Let's think about credit through the lens of thinking about what risks are involved with extending credit. In essence, the main risk that you were taking when you give someone credit is that the borrower doesn't pay you back. We quantify this risk by a term that we refer to as the expected loss given default. This may sound a little bit complicated, but it's really very simple. It is essentially just how much we expect to lose if the borrower doesn't pay us back. So the expected loss will be the probability that the borrower defaults multiplied by the percentage of the loss of the amount that is originally extended multiplied by how much of the loan is actually outstanding at default, which we refer to as exposure at default. Let's think about this through an example. Suppose we have a lender and she decides to make a loan of $10,000 for one year. She forecasts that there's a 2% chance that the loan will default. So 98 times out of 100, the borrower will pay the full $10,000 back, but 2 times out of 100, the borrower will not. We'll further assume that if the loan defaults, she gets a 0. And so in the previous example, the percentage loss in this case will be 100%. She'll lose the full $10,000. And the full $10,000 is payable in one year. This means that the exposure at default will be the full $10,000. What in this case is the expected loss? Again, from the formula that we had above, we take the $10,000 that is the exposure at default, multiply that by the 100% outstanding at the point of default and then multiply that by 2% to get $200, which is the expected loss. Now, we want to think about how this relates to the interest rate that a lender should charge in the previous example. First, let's start out by noting that investing in US Treasury Bills for one year might return in the neighborhood of about 2.5%. Again, reflect on the question, should she charge the borrower 2.5%? Notice that that 2.5% interest rate on Treasury Bills implies that if she invests $10,000, she can earn $250 nearly risk-free by investing in Treasury Bills. If she had makes the loan, she expects to lose $200. Therefore, the interest rate should compensate her for at least the opportunity cost $250 that she could get from investing in Treasury Bills and the expected loss of $200. And so the interest rate needs to compensate her for at least $450. In other words, she needs to charge an interest rate of at least 4.5%. However, we believe that lenders want to make some sort of profit and are averse to risk. They want to earn more than they lose and so as a result, we believe that lenders will charge some sort of a risk premium. Therefore, the final interest rate that our lender will charge will be this risk-free rate that is usually determined by US Treasury interest rates plus an expected loss plus the risk premium. And so as a result, when we think about an interest rate in this example, if the appropriate risk premium is 2%, our lender would charge 2.5% for the risk-free rate plus 2% for the expected loss plus 2% for a risk premium for a total loan rate of 6.5%. How could our borrower potentially reduce their interest rate? If we think about what is driving interest rates, they can't really affect the risk premium that they're being charged and they can't affect the baseline interest rate, the US Treasury rate. So what they can do is reduce the expected loss given default. If we think about the components of that expected loss given default, that means that we can reduce the probability of default, we can reduce the loss given default or we could reduce the exposure at default. Reducing the loss given default is usually achieved through collateral. Collateral is an asset that's pledged against the loan. When you take out a mortgage, you are using the house that you purchased as collateral for that mortgage borrowing. Posting collateral reduces loss given default because in that case, we give the collateral to the lender and the lender can liquidate the collateral to reduce the amount that they lose. So suppose as in our previous example, the borrower is borrowing $10,000 but uses a car as collateral. The lender expects to be able to sell the car for $5,000. How does this affect the expected loss? Well, now, our expected loss is 2%, the probability of default multiplied by the expected loss given default, which is $5,000. And our expected loss has now been reduced to $100 instead of $200. And so as a result, the borrower could potentially get a lower interest rate by reducing this expected loss. Another way to think about reducing expected loss is to reduce the exposure at default. And one way potentially to do this is to make periodic payments. In our original example, our lender asked for $10,000 due as a lump sum at the end of one year but suppose instead that she asked for $2500 in payments every three months. The borrower defaults under payments in 9 months, but she's already made two payments of $2,500. And so as a result, the exposure at default is only $5,000 instead of $10,000. In our next set of videos, we'll delve a little bit more into CreditTech per se. CreditTech largely focuses on assessing the probability of default. In our next lessons, we'll discuss how this technology has changed the process of underwriting loans.