ENHANCING FINANCIAL SECURITY: CNN AND EFFICIENTNET-BASED CREDIT CARD FRAUD DETECTION SYSTEM
DOI:
https://doi.org/10.29121/granthaalayah.v11.i12.2023.6114Keywords:
Financial, Detection, Security, System, Cnn, FraudAbstract [English]
This project report presents the design, development, and implementation of a credit card fraud detection system leveraging advanced deep learning techniques, specifically Convolutional Neural Networks (CNNs) and EfficientNet. The rapid growth of e-commerce and digital payment platforms has led to a surge in financial fraud, making it imperative to develop robust and intelligent mechanisms for early fraud detection. Unlike traditional machine learning methods, deep learning models offer enhanced capabilities in capturing complex patterns and subtle anomalies in high-dimensional transaction data.
In this work, CNNs are utilized to extract hierarchical spatial features from transaction data represented in structured or image-like formats, enabling the model to detect intricate fraud signatures. Furthermore, EfficientNet, a cutting-edge CNN architecture known for its balance of performance and computational efficiency, is applied to improve detection accuracy while maintaining low resource overhead. The model is trained to analyze historical customer transaction data, learn behavioral patterns, and identify irregularities indicative of fraudulent activity.
The proposed system focuses on real-time fraud detection in streaming transaction environments, integrating anomaly detection methods with deep learning for improved responsiveness and precision. This project ultimately contributes to the development of an adaptive and scalable fraud detection framework, capable of enhancing security in the financial domain and protecting users against evolving fraudulent threats.
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Copyright (c) 2023 Sarabjeet, Abhay, Kavita
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