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 be a major contributor to a person's credit score. The article also discusses the effects on credit scores of certain credit characteristics, and high-cost credit.
Time series evolution of credit scores
The time series component of many credit decisioning tools is crucial. These data help lenders assess the risk of a consumer's credit by tracking how they pay their bills over time. Lenders may be able to see more detail about borrowers' past history of late payments by using time series data from credit card balances.
Although this data can be positive, it may also indicate a downward trend. This is particularly true for consumers who are at lower risk or have lower scores. The recent declines in the number of hard credit inquiries may be related to the renewed consumer focus on reducing spending and paying down debt.
Dropping credit characteristics in groups that are closely related has an impact
One study evaluated the effects of removing related credit characteristics from credit scores. This group of credit characteristics was dropped by 2.5 points, which is about one-fifth of an inch. People with lower credit scores had greater changes than those with higher credit scores.
A drop in one attribute from a credit rating had little impact on the average 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 held across the three scorecards.
Effects of adding other characteristics
The effects of age on credit scores has been the focus of credit score analysis. It is unclear what the effect of adding another characteristic to a model might be. To determine the effects of adding another characteristic to the model, each scorecard model was re-estimated and compared with the FRB base model.
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 could be dropped, which would lead to a decrease in model predictiveness.
High-cost credits have negative consequences
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. A third negative effect of high-cost borrowing is the impact it has on the borrower’s reputation.
High-cost credit can reduce the demand for standard sources of financing and can restrict future access to those sources. Second, high-cost loans can cause borrowers to choose high-cost, which is a higher risk category. This can be helpful for short-term needs but may limit the availability to standard sources.