An Ensemble Deep Learning Approach for Credit Card Fraud Detection using CNN and AdaBoost

Abstract

Detecting credit card fraud remains a critical issue in financial security, demanding models that excel on imbalanced tabular transaction data while balancing accuracy, interpretability, and efficiency. This paper proposes a novel hybrid CNN-AdaBoost framework, where convolutional neural networks extract intricate, complex patterns from raw transactional features, and AdaBoost performs robust iterative classification to refine predictions. We evaluate the approach on standard credit card fraud benchmarks, achieving an overall accuracy of 95.43% alongside a strong F1-score of 92.75%, surpassing baselines methods. Ablation studies also conducted to confirm synergy: standalone CNN yields 93.12% accuracy, while pure AdaBoost reaches 93.58%, highlighting how feature fusion addresses individual shortcomings in handling noise and class imbalance. The novelty stems from repurposing CNN’s convolution layers to capture local, complex patterns directly from raw tabular transaction data, feeding these rich representations into AdaBoost for adaptive error correction. This hybrid uniquely blends deep feature learning’s nuance with ensemble boosting’s efficiency and interpretability, outperforming standalone methods on imbalanced fraud detection. Prior works rarely fuse these for tabular domains, yielding our 95.43% accuracy benchmark. This provides valuable insights into the design of hybrid models for structured and tabular data classification tasks and establishes a strong benchmark for future research in this domain.