The article explores how errors in estimating risk parameters can affect credit risk measures. Researchers used simulations to measure the bias caused by estimation errors and developed a correction method. This correction ensures that the probability of default matches the desired confidence level, without needing to estimate additional parameters. The approach was tested using real data and found to be effective in adjusting for estimation errors in credit risk models.