Credit Scorecards

Credit scorecards are created with the help of
statistics. First, all past loan applications of
interested consumers are collected.

Then these applications are divided into two essential
categories.

The first one deals with the people who repaid their
loans in due time without much hassle.

The second one deals with those of the defaulted.

It is mandatory to compare the first group with the
second one to prepare an appropriate scorecard.

Credit scorecards provide a 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 fit enough and it did have drawbacks.
Then newer and improved techniques were applied to
maintain this method of comparing credits.

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.