Securing Transactions: A Novel Federated Approach to Credit Card Fraud Detection Using Metaheuristic Optimization Techniques

Abstract

Credit card transaction fraud has resulted in a massive loss to both consumers and banks in recent years.As a result, this research proposes an optimized framework for fraud detection.This framework will allow banks to construct fraud detection models using training data stored in their internal database.With this approach, financial institutions can collectively reap the benefits of a shared global model, which has seen more fraud than each bank alone, without sharing the dataset.Hence, the sensitive information of the cardholders is protected.The proposed optimization strategy focuses on decreasing communication costs when proceeding with federal training to accelerate convergence speed by optimizing the initial global model before the federated learning phase.Additionally, there is a significant degree of skewness in credit card data, which makes it difficult to predict fraudulent transactions.Unbalanced or skewed data is preprocessed using the resampling approach to obtain better results.This work uses seven meta-heuristic optimization algorithms.These algorithms' performance is documented along with a comparative analysis.The work is done in Python, and computation time, accuracy, precision, recall, F-measure, loss, and computation time are used to evaluate how well the algorithms perform.The experimental results show that the Heapbased Optimizer (HBO) with Federated Learning (FL) Model can achieve high detection performance and the minimum loss ratio across different datasets.The proposed HBO-FL framework achieves an average performance across three benchmark datasets of precision (0.9807), recall (0.9771), accuracy (0.9781), and F-score (0.9788).The results are on three publicly available datasets (European cardholders, BankSim, and Creditcardcsvpresent).For more reliability, the suggested approach is compared with the sex of the previous works.