You can see the impact of adding or removing credit characteristics by looking at their time series. These characteristics can be a major contributor to a person's credit score. This article discusses the effects of dropping certain credit attributes and the impact of high-cost credit scores on credit scores.
Time series evolution in credit scores
Many credit decisioning models use time series data. These data help lenders assess the risk of a consumer's credit by tracking how they pay their bills over time. Time series data, such as credit card balances, can provide lenders with a better understanding of borrowers' history of late payments.
While this data is generally positive, it can also show a downward trend. This is especially true for consumers in lower risk and lower scoring segments. 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 looked at the effect of removing a group of related credit characteristics from 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.
The mean black credit score was not affected by a single attribute being dropped from a credit score. The largest change in the mean black credit score was 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.
Traditionally, analysis of credit scores has only examined the effects of a single characteristic, such as age. It is unclear what the effect of adding another characteristic to a model might be. 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.
Although the average score did not change, adding race or ethnicity would affect the predictive value. However, dropping these attributes would result in a significant decrease in model predictiveness for other people.
High-cost credits have negative consequences
A credit score can be negatively affected by high-cost debt for many reasons. It signals lenders that the borrower is a high-risk credit risk. Second, high-cost loans can result in higher defaults. These defaults can have adverse effects on the overall financial position. A third negative effect of high-cost borrowing is the impact it has on the borrower’s reputation.
High-cost loans can limit the availability of standard sources of financing, and reduce demand. 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.