AI‐Driven Adaptive Federated Learning With Privacy Preservation and Imbalance Adjustment for Financial Credit Card Fraud Detection

Abstract

The increasing complexity of fraudulent activities requires advanced fraud detection systems, as existing solutions lack effectiveness due to two challenges. First, privacy concerns prevent financial institutions from sharing sensitive transaction data. Second, data imbalance causes biased models, as fraudulent transactions represent a small fraction of total transactions, leading to poor fraud detection performance. To address these challenges, we propose an AI‐driven adaptive federated learning (AFL) framework for credit card fraud detection (CCFD). AFL enables decentralized learning, allowing financial institutions to train a global fraud detection model collaboratively without sharing raw transaction data. The model aggregation is performance‐adaptive, weighting client contributions based on detection accuracy to ensure a robust global model. To overcome data imbalance, we introduce a multistep data balancing framework integrating Tomek links for undersampling, borderline‐SMOTE for oversampling, and cognitive sample pruning to remove misleading samples. To evaluate the robustness and generalizability of the proposed framework, we conducted experiments on both the widely used 2013 Kaggle dataset and the Sparkov simulated dataset (2019‐2020). The Sparkov dataset, which contains interpretable demographic and merchant‐level features, allowed us to test the model’s adaptability to diverse data sources. The results demonstrate that the proposed AFL framework, combined with advanced data balancing, significantly outperforms traditional models, achieving 99% accuracy, 99.5% precision, 99.4% recall, and 99% F1‐score on the Kaggle dataset, and 97.4% accuracy, 99.5% precision, 97.5% recall, and 98.4% F1‐score on the Sparkov dataset. This research highlights AI’s transformative role in finance, particularly in enhancing fraud detection systems with improved accuracy, robustness, security, and scalability.