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Gradually, though, the credit scorecard has been replaced by a certain method that actually has several names.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’ ability to process the computation of the financial value of the loan. All that is needed for the computation is the risk level, taken from the credit viewpoint. You have to remember that the database takes into consideration each and every possible observation, regardless of the default or non-default nature. This makes it all the more easier to recognize the results of what are known as macro-economic aspects, including interest rates, auto prices, stock prices, and more.

Credit scorecards present a more direct and accurate approach towards the assessment of a company’s credit risk. All you have to do is furnish the latest financial statement of your company and then you can compute for the diverse financial ratios. Factors included in the computation of these ratios are current ratio, profit before tax or net profit rate, long term or gearing creditors, trade debtors, interest cover, and stock turn.

To sum, credit scorecards are indeed very important in ensuring the growth and prosperity of financial institutions, not to mention the financial security of the enterprise as a whole. Given these very important roles to play, it is then quite obvious how banks should invest in the implementation of such tools.