As mentioned previously, empirical models of the probability of default are used to compute an individual’s default probability, applicable within the retail banking arena, where empirical or actual historical or comparable data exists on past credit defaults. The dataset in Figure 2.5 represents a sample of several thousand previous loans and credit or debt issues. The data shows whether each loan had defaulted or not (0 for no default, and 1 for default), as well as the specifics of each loan applicant’s age, education level (1–3 indicating high school, university, or graduate professional education), years with current employer, and so forth. The idea is to model these empirical data to see which variables affect the default behavior of individuals, using Risk Simulator’s Maximum Likelihood Estimation (MLE) tool. The resulting model will help the bank or credit issuer compute the expected probability of default of an individual credit holder of having specific characteristics.