Smart Detection of Credit Card Fraud Using Deep Learning Technique

As digital payment platforms become more widely used, the growing incidence of credit card theft presents a serious threat to global financial infrastructures. This study introduces an advanced fraud detection model utilizing Artificial Neural Networks (ANNs), developed to detect suspicious transaction behaviors. ANNs are particularly useful in these applications because of their ability to learn complex, non-linear patterns from high-dimensional inputs. A sanitized dataset that included transactional factors like timing, amount, location, and user behavior indications was used to train the model. Techniques like Synthetic Minority Over-sampling (SMOTE) and normalization were utilized to tackle the fundamental issue of data disparity in fraud detection. Exhibiting high detection accuracy and minimal false-positive rates, the model's assessment using key performance indicator Accuracy, Precision, Recall, and F1-Score outperformed conventional approaches. The promise of AI-enhanced models to strengthen the security of digital financial systems in confirmed by these findings.