Estimating Credit Scores with Logit
Typically, several factors can affect a borrower's default probability. In the retail segment, one would consider salary, occupation and other characteristics of the loan applicant; when dealing with corporate clients, one would examine the firm's leverage, profitability or cash flows, to name but a few items. A scoring model specifies how to combine the different pieces of information in order to get an accurate assessment of default probability, thus serving to automate and standardize the evaluation of default risk within a financial institution.
In this chapter, we show how to specify a scoring model using a statistical technique called logistic regression or simply logit. Essentially, this amounts to coding information into a specific value (e.g., measuring leverage as debt/assets) and then finding the combination of factors that does the best job in explaining historical default behavior.
After clarifying the link between scores and default probability, we show how to estimate and interpret a logit model. We then discuss important issues that arise in practical applications, namely the treatment of outliers and the choice of functional relationship between variables and default.
An important step in building and running a successful scoring model is its validation. Since validation techniques are applied not just to scoring models but also to agency ratings and other measures of default risk, they are described separately in Chapter 8 ...