FICO Scorecards Analysis
FICO Scorecards Very Sophisticated
This Essay is for the more advanced student of credit repair. Some good background and study of credit repair on the part of the reader is expected for this one. I like to look under the hood for accurate answers to credit repair questions. I check the actual laws and internal FICO Scorecards memoranda to separate the real facts and evidence from the guesswork. I don’t regurgitate tired old questionable rewrites of other people’s mistakes as many so called credit repair experts often do. FICO scorecards are too important for guesswork.
FICO’s credit scoring system is both art and science. The framework used to construct your credit score consists of the most sophisticated mathematical modeling you are likely to see anywhere. If you are not an MIT or Cal Tech graduate student you will get lost reading one of their FICO Scorecards memorandums. Think I’m kidding? Are you comfortable discussing terms such as:
- Robust modeling
- Dirty data
- Data distortions
- Amelioration of selection bias
- Fitting Objective Function
- Range Divergence
- Score scaling
- Variable binning
If these are the kinds of terms you regularly drop into your cocktail party conversations then you might consider applying for a lucrative position at the FICO credit scoring giant. The rest of us need something more down to earth that we can grasp. We want to increase our ability to get and keep a super prime FICO score. Super prime means 780 or above. This score is as good as it ever needs to be for the best available rates on home mortgages. 760 will do for auto loans.
FICO Uses “Bins” to Develop FICO Scorecards
FICO states on its web site that it has over 60 customized versions of its score to offer its customers. These 60 score versions can be further fine tuned and customized according to the customers desire. The scoring process starts by examining the consumer’s credit history and arranging the key components into “bins” for better or worse. Have a public record such as a tax lien or a bankruptcy? That’s 1 bin. The number of accounts is another bin, the age of the oldest account another, the average age of accounts another. If you have had a recent serious delinquency such as 60 days late that dubious distinction is yet another. And of course charge offs, collections, judgments and repossessions are isolated for analysis within the whole picture that will ultimately compose the score.
FICO Bins interlock in complicated ways
The characteristics of the borrowers in the bins have been carefully analyzed using historic data that determines how likely the individual borrower is to default on his loans. In other words the other occupants of your different bins have similar borrowing characteristics, history and habits. A typical FICO Scorecards model may consist of 10 bins that look like this:
- Public records or bankruptcy
- Serious delinquencies, late payments, collections, judgments, charge offs, repossessions
- Only 1 credit account (very thin file)
- Only 2 credit accounts (thin file)
- Only 3 credit accounts
- Under 2 years for oldest account
- 2-5 years for oldest account
- 5-12 years for oldest account
- 12-19 years for oldest account
- 20 years or more for oldest account
Now imagine a network of lines crossing from one bin to another. The numbers generated are analyzed by FICO’s computer, made into FICO Scorecards and we have a score!
The average consumer may never realize why his score jumped when he hit a certain threshold such as his oldest account hitting 6 years or a charge off falling off the record after 7 years. That threshold meant nothing to the consumer but it was of great interest to the computer. This explains why you don’t see slow upticks in your score as accounts age. The benefits of older accounts only accrue when the age hits certain markers.
Another example: The negative effects of too many inquiries disappear from the FICO Scorecards algorithm on the 366th day after the inquiry was made. An 11 month old inquiry has exactly the same effect as a 1 month old inquiry!