Credit card dodging is the act of intentionally avoiding payment obligations on a credit card, often through missing payments, fraudulent behaviour, or other evasive tactics. This practice is illegal and can result in severe financial and legal consequences. It is a significant issue within the financial sector, affecting both lenders and borrowers. Traditional methods of identifying credit card defaulters often rely on individual credit ratings and payment histories, which may not suffice for reliable predictions, especially given the complexity and volume of credit card transactions. This study proposes a novel approach combining logistic regression models with visual analysis techniques such as pivot tables and subplots to enhance the prediction of credit card defaulters. Logistic regression was chosen for its ability to model the probability of default based on multiple predictor variables, while pivot tables and subplots were used to uncover data patterns and anomalies. The model’s performance is assessed using metrics like accuracy, precision, and recall, demonstrating improved predictive power when augmented with these visual techniques. The proposed approach not only achieves higher accuracy and precision compared to traditional methods but also provides a practical tool for financial institutions to better manage credit risk.