It is possible to examine the time-series evolution of credit scores and see how it affects credit attributes. These characteristics can be a major contributor to a person's credit score. The article also addresses the effects of dropping credit characteristics and the effects high-cost credits have on credit scores.
Time series evolution credit scores
In many credit decisioning models, time series data is an important component. This data helps lenders determine the risk of a consumer by tracking how a consumer pays his or her bills over time. Time series data, such as credit card balances, can provide lenders with a better understanding of borrowers' history of late payments.
This data is generally good, but it can also show a downward tendency. This is especially true of consumers in lower risk segments and lower scoring. Recent declines of hard credit inquiries may be due in part to consumer's renewed focus on reducing debt and spending.
Effects of dropping groups of related credit characteristics
One study evaluated the effects of removing related credit characteristics from credit scores. The average credit score was able to be lowered by 2.5 points or approximately one-fifth. The changes were larger for people with younger credit scores than for people with older credit scores.
The mean black credit score was not affected by a single attribute being dropped from a credit score. The largest increase in black credit score was only 0.1 point. The high correlation between these characteristics in our scoring model is what explains this small change. These differences held across the 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. While it is difficult to know the effects of adding more characteristics, they may have a significant impact on the model. To assess the effect of adding another attribute, the scorecard models were re-evaluated using the new characteristic. The results were compared to FRB's base model.
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, dropping these attributes would result in a significant decrease in model predictiveness for other people.
High-cost credit: The effects
There are several reasons why taking on high-cost debt can negatively affect a credit score. The first is that it sends a signal to lenders about a borrower's poor credit score. High-cost borrowing can lead to more defaults which can have negative effects on overall financial health. A third negative effect of high-cost borrowing is the impact it has on 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. Although this can help with short-term financial issues, it also limits the availability for standard sources of financing.