In our previous video, we discussed an example of a lender who made a $10,000 loan. In making her loan, she assessed the probability of default as two percent. In this video, we want to discuss, or at least start discussing how she made the assessment that the probability of default would be two percent. In traditional underwriting, lenders often use a rule that I'll call the 28/36 rule, 28 percent/36 percent of income. This rule essentially says that if a loan's payment is less than 28 percent of income, and all payments are less than 36 percent of income, the borrower is eligible for that loan. That is, we say that the borrower has the capacity to repay that loan. To illustrate, let's suppose we have a borrower with $5,000 per month of income. She currently has debt payments of $500 per month, or 10 percent of her total income. Twenty eight percent and 36 percent of her income are $1,400 and $1,800 per month respectively. So given her current debt burden of $500 in payments per month, our lender would extend a loan of no more than $1,300 per month. Again, to think about this, the amount of the loan payment has to be less than $1,400, which is 28 percent of her income. But the total amount of her payments, $500 plus the amount of the loan, has to be less than $1,800, and so $1,800 minus $500 gives us the maximum loan payment of $1,300 per month. In addition to the capacity to repay, a lender will typically also consider other information. For example, the lender might look at how long the borrower had been employed. A long employment record suggests that the borrower has a stable income, and can continue to repay the loan. What liquid assets does she have available? These are things that the borrower can easily access if she has difficulty in repaying the loan to make her loan payments, and what does her history of payments look like? Does she have a history of missing payments? If so, the lender may assess the likelihood of missing future payments as being higher as well. These pieces of information are used together to create a subjective decision as to whether the lender is willing to extend the loan or not. Because this is a subjective decision, traditional underwriting involves a lot of judgment calls. It's a bit of a one size fits all approach to credit extension, rather than being tailored to individual borrowers. It has led historically to practices such as redlining, where mortgage lenders would put a red mark around an entire neighborhood and refuse to provide loans to residents in that particular neighborhood. It also depended on frequently suspect credit reporting information. What do I mean by credit reporting? The idea of credit reporting originates from the early 19th century in England. At that time, tailors would get together, and give each other information about their customers and whether those customers paid their bills or not. Eventually, this information was put together into a newsletter, rather than being simply word of mouth, and these newsletters became the precursor of the modern credit bureau. The modern credit bureau has its roots in a set of companies that collected information about borrowers on card files. There are currently three major companies that provide credit bureaus; TransUnion, Experian, and Equifax. But they all have roots in much older companies. Unfortunately, there were often allegations up through about the 1970s, that these credit reporting agencies collected information about borrowers in an inappropriate way, and that much of the information was simply rumor or hearsay. This concern about the traditional credit reporting agencies led to the creation of FICO. FICO was created by Bill Fair and Earl Isaac to form Fair, Isaac and Company in 1956. Fair and Isaac were an Engineer and Mathematician who believed that data analytics could lead to better credit decisions. What they aspire to do was to remove the subjectivity and misuse of information from credit decisions. So avoiding the subjective information provided by the credit reporting agencies, and instead, simply use data and data analytics in order to make these credit decisions for individuals. From Fair and Isaac, we now are fairly familiar with the concept of FICO scores. A FICO score ranges from 300 to 850, and is based on five factors. The first is the borrowers payment history, which represents 35 percent of the credit score. This is essentially a documentation of how frequently you have paid your bills on time, how frequently you've been late on your bills, or how frequently you've failed to pay your bills at all. The second factor is utilization, which accounts for 30 percent. This essentially captures how much of revolving credit, so things like credit cards, you actually use. The higher the balance you usually maintain on your credit card, the worse your score. The third component is history length. So a borrower with a longer credit history will typically have a higher score. The fourth factor is credit inquiries, and the concern here is that borrowers are potentially simply opening new credit lines in order to pay off old credit lines. The fifth factor is the credit mix, which represents the idea of the types of credit you have outstanding revolving such as credit cards or installment such as car loans and mortgage payments. The score itself is meant to reflect the probability that you will go 90 days past due on an account in the next two years. So again, this credit score is a direct reflection of the probability of default that we discussed in the past video. The computation of the FICO score is really unknown. So it's what we call a proprietary model. It's owned by FICO, and we don't know how the score is actually calculated. However, we know something about how to use data analytics to gauge the probability of an event occurring. In our next video, we'll talk about how one could use data analysis to come up with a credit score.
