Credit Scorecards
From Wikipedia, the free encyclopedia
Credit Scorecards are mathematical models which attempt to provide a quantitive measurement of the likelihood that a customer will display a defined behavior with respect to their current, or proposed, credit position with a lender.
Historically, credit scoring has typically been based on a data base built using observations on former clients who defaulted on their loans plus observations on a large number of clients who have not defaulted. Statistically, estimation techniques such as logit or probit are used to create estimates of the probability of default for observations based on this historical data. This model can be used to predict probability of default for new clients using the same observation characteristics. The default probabilities are then scaled to a "credit score." This score ranks clients by riskiness without explicitly identifying their probability of default.
There are a number of credit scoring techniques such as: hazard rate modeling, reduced form credit models, weight of evidence models, linear or logistic regression. The primary differences involve the assumptions required about the explanatory variables and the ability to model continuous versus binary outcomes. Some of these techniques are superior to others in directly estimating the probability of default. Despite much research from academics and industry, no single technique has been proven superior for predicting default in all circumstances.
