The main characteristic of this model is its reliance on financial ratios. A statistical technique is used in order to assign risk weights to several financial ratios that differentiate between defaulting and successful companies. For example, 22 financial ratios were tested while developing the Altman Model (1968), which is widely used both in the academic literature and in practice.

A logit model is a popular statistical model, which is used widely for the measurement of PD for corporate customers, mainly for two reasons. First, the output from the logit model can be directly interpreted as the probability of default. Second, this model can be verified easily. Hence, recommendation is to use a logistic model.

The event of default must be clearly defined. Historically, the definition used for rating models was bankruptcy, as this information was readily available and this type of model is powerful in predicting. However, the definition of default may include delays in payments and other situations in which the bank does not receive full payment.

Ratios are calculated to standardize the available information. For example, the ratio “Earnings per Total Assets” enables to compare the profitability of firms of different sizes. In addition to calculating ratios that reflect different financial aspects of the borrowers, dynamic ratios that compare current and past levels of particular balance sheet items can be extremely useful in predicting the event of default. Input ratios represent the most important credit risk factors (leverage, liquidity, productivity, turnover, level of activity, profitability, firm size, growth rates and leverage development).

This is a classification model, which is used to decide which of the existing customers is in danger of defaulting in the near or medium-term future

Behavior scoring models are derived from a retrospective statistical analysis of the credit performance of individual accounts. The purpose of the statistical analysis is to find the most predictive set of data elements that distinguish between the good credit risks from the poor credit risks. Behavior scoring models evaluate the creditworthiness of existing customers. The output of behavior models is the probability that an ongoing account will be delinquent and/or written-off and/or experience bankruptcy and/or sent to a collection agency and/or exhibit some other type of derogatory payment behavior over a specified period of time (behavior probability). Behavior models are effective risk management tools and can be used to adjust credit limits and decide on the marketing and operational strategy to be applied to each customer.

The extra performance variables in behavioral scoring systems include the following variables: the current balance owed by the account and various averages on this balance, the amount repaid by the account during the last month, six months, etc, the amount of new credit extended and the usage of credit facilities over similar periods. Over variables refer to the status of the account. For example, the number of times it had exceeded its credit limit, the number of dunning letters that had been sent, and the time that had passed since the last repayment had been made. Thus, there can be a large number of similar performance variables with strong correlation. The statistical convention is to include only a few of these similar variables in the scoring system and to use only those that have the greatest impact.

A common definition of a bad account is an account that has missed three, possibly consecutive, months of payments during the outcome period.