The time series evolution of credit scores provides a great way to see the effects of removing or adding certain credit characteristics. These attributes can make a big difference in a person’s credit score. The article also discusses the Effects of dropping certain credit characteristics and the effect of high-cost credit on credit scores.
Time series evolution of credit scores
Time series data is a critical component of many credit decisioning models. This data is used by lenders to determine the risk of a customer's credit history. It tracks how a consumer pays 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 particularly true for consumers who are at lower risk or have lower scores. A recent drop in hard credit inquiries might be due to increased consumer attention on reducing spending and decreasing debt.
Dropping credit characteristics in groups that are closely related has an impact
One study examined the impact of removing credit characteristics that are related to a credit score. Dropping the credit characteristics in question raised the average credit score by 2.5 points. That's about one-fifth. The changes were larger for people with younger credit scores than for people with older credit scores.
Dropping a single characteristic from a credit score had very little effect on the mean score for blacks. The most significant change in the average black credit score was 0.1 points. This is due to the high correlation of these attributes in the scoring system. These differences were consistent across all three scorecards.
Other characteristics may have an adverse effect on your ability to perform.
Analyzing credit scores has traditionally focused only on one characteristic. For example, age. 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 mean score was not affected by the addition of ethnicity or race, but it would have an impact on the predictive power. These attributes can be removed, but it would have a significant impact on model predictiveness.
Effects of high-cost credit
A negative credit score can result from several factors. High-cost debt signals to lenders that a borrower has poor credit ratings. Second, high-cost borrowing results in more defaults, which in turn can have negative consequences on the overall financial situation. Third, high-cost credit has a negative effect on the social reputation of the borrower.
High-cost credit may reduce the demand for traditional sources of financing and could limit future access. High-cost credit can also lead to borrowers choosing high-cost credit as a more risky option. This may be a good option for short-term financial problems, but it can also limit the availability of traditional sources of financing.