The rise of the digital economy has emphasized the importance of data quality in the credit sector. This study evaluates the effectiveness of three machine learning models—Isolation Forest, One-Class Support Vector Machine (SVM), and Autoencoder—in detecting corrupted records in loan datasets. The primary goal is to test different machine learning models in the perspective of data quality for the banking sector. Utilizing a dataset from Kaggle, intentional data corruption was introduced to simulate real-world scenarios. Isolation Forest demonstrated superior performance with a perfect recall (1.00) for corrupted samples and an overall accuracy of 0.91, despite a moderate precision (0.50). One-Class SVM showed balanced but moderate performance, with recall and precision values for corrupted samples at 0.51 and 0.34, respectively. Autoencoder underperformed, achieving a recall of 0.09 and a precision of 0.02 for corrupted samples. Computational efficiency analysis highlighted Isolation Forest’s low training and testing times. These findings underscore the need for model selection tailored to specific dataset characteristics and operational requirements. This study was conducted using Python 3.12, Scikit-learn 1.4.2, TensorFlow 2.16.1, on a 12th Gen Intel® Core™ i9-12900H Processor. Limitations include the synthetic nature of data corruption and potential bias in model selection due to parameter settings and computational constraints.