The Logic Behind Credit Scorecards
Credit scorecards have long been important tools used by banks, lending companies, and other financial institutions. There are many reasons why the credit scorecard is regarded a very important tool. One of the reasons is that the credit scorecard actually serves as a quantitative model that is geared towards providing measurements of the likelihood that a certain client can demonstrate a particularly defined behavior regarding his present credit standing with a particular lender. In simpler terms, the credit scorecard contains quantifiable aspects that make it easier to measure the likelihood of a borrower behaving in a particular manner, regarding his debt to a lender.
The basis of credit scoring is actually pretty simple. It is actually derived from a database that has been developed to monitor observations of the behavioral patterns of previous clients who have resorted to loan defaults. Loan defaults are just about the worst case scenario a financial institution can experience with any of its clients. This is because the when a client defaults his or her loan, it means that the client has declared financial incapacity to pay off that loan. The default probabilities are then scaled to respective credit scores. The credit score then becomes a ranking system of the clients with risk directing its order or sequence. This way, only the credit score, which is figurative counterpart of the default probability, would be exposed, and not the default probability itself. Since the inception of the credit scorecard, it has been used by many banks and financial institutions all over the world.
Gradually, though, the credit scorecard has been replaced by a certain method that actually has several names. These names include logistic regression, reduced form credit models, and hazard rate modeling. The more recent models have several new features that distinguish them from credit scorecards. For starters, the more recent models come with the database itself, which includes the latest and historical observations of credit behavioral patterns. Another significant feature to take note of is the modern models
