Dismantling Binary Opposition in Fraud Detection: A Fuzzy Deep Learning Framework for Imbalanced Transaction Data

Abstract

In the context of behavioral finance, detecting credit card fraud remains a critical challenge, particularly when dealing with highly imbalanced datasets and ambiguous transaction patterns. This complexity highlights the limitations of traditional fraud detection models, which rely on a rigid binary distinction between “fraudulent” and “legitimate” transactions. Such a perspective restricts analysts’ ability to capture the nuanced and uncertain nature of fraudulent behavior, underscoring the need for a more flexible and practical approach. Accordingly, this study draws on Derrida’s deconstructive philosophy of binary oppositions to challenge the dominant dichotomy underlying conventional detection systems. This perspective provides a theoretical foundation for rethinking fraud detection by operationalizing deconstructive principles through the integration of fuzzy rules and machine learning architectures. The proposed approach is designed to address uncertainty, class imbalance, and semantic instability in financial transaction data. By combining fuzzy logic with deep learning, the framework deconstructs the rigid binary classification of transactions, enabling interpretation along a spectrum of legitimacy rather than as mutually exclusive categories. Deep learning techniques identify complex, nonlinear patterns that reveal overlaps between fraudulent and legitimate behaviors, while fuzzy membership functions model uncertainty and capture borderline cases that cannot be effectively handled by binary classification.