How Data Quality Affects Machine Learning Models for Credit Risk Assessment
This work addresses data quality challenges for practitioners in financial risk modeling, but it is incremental as it applies existing corruption methods to a specific domain.
The paper investigates how data quality issues such as missing values, noise, outliers, and label errors affect the predictive accuracy of machine learning models for credit risk assessment, finding significant differences in model robustness based on the type and severity of data degradation.
Machine Learning (ML) models are being increasingly employed for credit risk evaluation, with their effectiveness largely hinging on the quality of the input data. In this paper we investigate the impact of several data quality issues, including missing values, noisy attributes, outliers, and label errors, on the predictive accuracy of the machine learning model used in credit risk assessment. Utilizing an open-source dataset, we introduce controlled data corruption using the Pucktrick library to assess the robustness of 10 frequently used models like Random Forest, SVM, and Logistic Regression and so on. Our experiments show significant differences in model robustness based on the nature and severity of the data degradation. Moreover, the proposed methodology and accompanying tools offer practical support for practitioners seeking to enhance data pipeline robustness, and provide researchers with a flexible framework for further experimentation in data-centric AI contexts.