The time series evolution of credit scores provides a great way to see the effects of removing or adding certain credit characteristics. These characteristics can be a major contributor to a person's credit score. The article also discusses how credit can affect credit scores and the effects of high-cost debt.
Time series evolution in credit scores
Many credit decisioning models use time series data. By tracking how consumers pay their bills over time, lenders can determine the consumer's risk. Lenders can gain a greater understanding of borrowers' late payments history by looking at time series data on their credit card balances.
The data is generally positive, but it could also indicate a downward trend. This is especially true for consumers with lower risk and lower scores. A recent drop in hard credit inquiries might be due to increased consumer attention on reducing spending and decreasing debt.
Effects of dropping groups of related credit characteristics
One study evaluated the effects of removing related credit characteristics from credit scores. Dropping the credit characteristics in question raised the average credit score by 2.5 points. That's about one-fifth. These changes were greater for those with lower credit scores than for those with higher credit scores.
A single credit score characteristic that is not included in the black average score has very little effect. The most significant change in the average black credit score was 0.1 points. The reason for this small change is the high correlation of these characteristics in the scoring model. These differences were consistent across all three scorecards.
Other characteristics may have an adverse effect on your ability to perform.
The effects of age on credit scores has been the focus of credit score analysis. Although it is not known how adding additional characteristics to a model can affect its effectiveness, it may be significant. The model for each scorecard was reevaluated with the additional characteristic. It was then compared to the FRB base modeling.
The average score does not change if you add race or ethnicity to your model. However, it will have an effect upon the predictive value. However, removing these attributes would cause a significant decrease of model predictiveness for others.
High-cost credit has negative effects
A negative credit score can result from several factors. First, it signals to lenders that a borrower is a poor credit risk. Second, high-cost borrowing results in more defaults, which in turn can have negative consequences on the overall financial situation. The third effect of high-cost credit is on the borrower’s social reputation.
High-cost financing can decrease the demand for standard sources and may limit future access to these sources. Secondly, high-cost credit causes borrowers to self-select into high-cost credit, a riskier category. While this may help alleviate short-term financial needs, it limits the availability of standard sources of financing.