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.
A particular point of time is chosen as the observation point. A period preceding the observation point, for example the previous 12 to 18 months (minimum 6 months) is chosen as the performance period. The characteristics of performance during this period are used as explanatory variables. A point in time, for example, 12 months after the observation point is chosen as the outcome point. The customer is classified as good or bad depending on its status at the outcome point.
Recommended longevity of data - 5 years