Time series evolution of credit scores is a great tool to understand the effects of removing certain credit characteristics or adding them. These characteristics can contribute significantly to a person’s score on credit. The article also discusses how credit can affect credit scores and the effects of high-cost debt.
Time series evolution credit scores
The time series component of many credit decisioning tools is crucial. This data helps lenders determine the risk of a consumer by tracking how a consumer pays his or her bills over time. For example, time series data on credit card balances can give lenders a more detailed view of a borrower's history of late payments.
The data is generally positive, but it could also indicate a downward trend. This is particularly true for consumers who are at lower risk or have lower scores. Recent declines in hard credit inquiries could be due to a renewed consumer focus on reducing spending, and paying down their debt.
The impact of dropping credit characteristics related to groups
One study looked at the effect of removing a group of related credit characteristics from a credit score. The mean credit score rose by 2.5 points (or about one-fifth) when this group of credit attributes was removed. 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 largest change in the mean black credit score was 0.1 point. These characteristics are highly correlated in the scoring model, which explains the small change. These differences are consistent across all three scorecards.
Other characteristics can have an effect
Analyzing credit scores has traditionally focused only on one characteristic. For example, age. The effects of adding other characteristics are not well understood, but adding another characteristic to the model may have a significant effect. To determine the effects of adding another characteristic to the model, each scorecard model was re-estimated and compared with the FRB base model.
Although the average score did not change, adding race or ethnicity would affect the predictive value. These attributes can be removed, but it would have a significant impact on model predictiveness.
High-cost credit: The effects
Credit score can be adversely affected by taking out high-cost loans for several reasons. It signals lenders that the borrower is a high-risk credit risk. A second consequence of high-cost borrowing is more defaults. This can have negative financial consequences for the overall financial picture. Third, high-cost loans can negatively impact the borrower's reputation.
High-cost credits can lower the demand and limit future access to standard sources. A second reason is that high-cost borrowers may choose to take out high-cost financing, which can be more risky. This may be a good option for short-term financial problems, but it can also limit the availability of traditional sources of financing.