Credit scorecards are created with the help of
statistics. First, all past loan applications of
interested consumers are collected.
The first one deals with people who repaid their
loans on time without much hassle.
It is mandatory to compare the first group with the second one to prepare an appropriate scorecard.
Credit scorecards provide an accurate measurement of the likelihood that a customer will repay the credit amount back in the allowed amount of time.
Logit or probit are estimation techniques which are statistically used to predict the probability of default of new clients based on this historical data base.
The default probabilities are then compared to a 'credit score.' This score will rank the potential client by their height of risk without explicitly identifying their probability of default.
It is to be noted that the procedure of credit scoring was not always good enough and it did have drawbacks. Then newer and improved techniques were applied to maintain this method of comparing credit.
These measures are: hazard rate modeling, reduced form credit models, or logistic regression.
The essential differences from credit scoring involve both the data base and the ability to calculate the financial value of a loan, given its risk from a credit perspective.
The data base includes all of the available observations on both defaulted and non-defaulted clients. This makes it much easier to see the effects of macro-economic factors like stock prices, auto prices, interest rates, and home values on the default rates of retail loans secured by automobiles or homes.
negotiate debts, and win with the credit game