New Methods for Cleaning Borrower Data in Credit Models

Data scientists are developing Python techniques to handle outliers and missing values in credit scoring models. Robust data preprocessing is critical for accurate lending decisions and fair risk assessment. These methods help prevent biased models that could discriminate against borrowers.