With the widespread use of credit cards in online and offline transactions, credit card fraud has become a significant challenge in the financial sector. The rapid advancement of payment technologies has led to increasingly sophisticated fraud techniques, necessitating more effective detection methods. While machine learning has been extensively applied in fraud detection, the application of deep learning methods remains relatively limited. Inspired by brain-like computing, this work employs the Continuous-Coupled Neural Network (CCNN) for credit card fraud detection. Unlike traditional neural networks, the CCNN enhances the representation of complex temporal and spatial patterns through continuous neuron activation and dynamic coupling mechanisms. Using the Kaggle Credit Card Fraud Detection (CCFD) dataset, we mitigate data imbalance via the Synthetic Minority Oversampling Technique (SMOTE) and transform sample feature vectors into matrices for training. Experimental results show that our method achieves an accuracy of 0.9998, precision of 0.9996, recall of 1.0000, and an F1-score of 0.9998, surpassing traditional machine learning models, which highlight CCNN’s potential to enhance the security and efficiency of fraud detection in the financial industry.