Credit scores today are generated by a complex series of algorithms designed to assess the risk that a borrower will have a late payment within 24 months. Though every borrower is assessed by the same parameters to generate a credit score, these parameters are weighted diff erently depending on the borrowers’ characteristics and how well the borrower fits one of ten profiles. Specific profiles are reserved for individuals with even just one 90-day late payment or bankruptcy in their credit history. Mortgage lenders tie their rates directly to credit scores.
Components of the score
Though the model evaluates each data element with respect to “Recent-cy”, “Frequency” and “Severity,” the most important of these perspectives is recent-cy.
35% - Payment History
The perspective weighted most heavily in assessing payment history is recent-cy.
Age of last late payment < months =" big" months =" less" months =" signifi"> 50% but <> 75% = big hit
15% - Credit History
This portion of the score assesses the number of new trade lines and their respective ages. If trade lines need to be closed, careful consideration must be made to the effects of trade line age per credit amount extended. The most credit-scoring-wise decision would be based on the reason codes in the individual report making specific accommodation for individual circumstances.
10% - Type of Credit
This portion of the score reflects what the scoring engine determines a “good” mix of credit types. What is “good” in one profile type may not be as good if assessed according to another profile type. Across all profiles, however, finance company credit lines are negatively assessed.
10% - Inquiries
Because the engine assumes that a person applying for multiple trade lines within a short period of time may be indicative of fi nancial trouble, this portion of the scoring engine examines the number of inquiries over time made to an individual’s credit report. The scoring engine treats certain economic sectors diff erently than credit line issues. If an inquiry is made by a lender within the last 12 months, the mortgage financier gets a 30 day window during which time additional inquiries will not negatively affect the score.
Components of the score
Though the model evaluates each data element with respect to “Recent-cy”, “Frequency” and “Severity,” the most important of these perspectives is recent-cy.
35% - Payment History
The perspective weighted most heavily in assessing payment history is recent-cy.
Age of last late payment < months =" big" months =" less" months =" signifi"> 50% but <> 75% = big hit
15% - Credit History
This portion of the score assesses the number of new trade lines and their respective ages. If trade lines need to be closed, careful consideration must be made to the effects of trade line age per credit amount extended. The most credit-scoring-wise decision would be based on the reason codes in the individual report making specific accommodation for individual circumstances.
10% - Type of Credit
This portion of the score reflects what the scoring engine determines a “good” mix of credit types. What is “good” in one profile type may not be as good if assessed according to another profile type. Across all profiles, however, finance company credit lines are negatively assessed.
10% - Inquiries
Because the engine assumes that a person applying for multiple trade lines within a short period of time may be indicative of fi nancial trouble, this portion of the scoring engine examines the number of inquiries over time made to an individual’s credit report. The scoring engine treats certain economic sectors diff erently than credit line issues. If an inquiry is made by a lender within the last 12 months, the mortgage financier gets a 30 day window during which time additional inquiries will not negatively affect the score.
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