A Suite of Interpretable Hybrid Systems with a Voting Mechanism for Detecting Financial Fraud in Digital Forensics
DOI:
https://doi.org/10.59746/qr58vk09Keywords:
Financial Fraud Detection, Digital Forensics, Decision Tree, Fuzzy Inference System, Explainable AI, Hard VotingAbstract
Financial fraud has become a critical challenge in modern digital payment systems as financial transactions increasingly rely on online platforms. Despite advancements in payment technologies, detecting fraudulent activities remains difficult due to evolving attack strategies and system vulnerabilities. Traditional fraud detection methods suffer from high false positive rates, inability to adapt to new fraud patterns, and limited interpretability, especially when using complex models that function as black boxes. This study proposes a multilayered fraud detection framework that integrates Decision Tree (DT) and Fuzzy Inference System (FIS) within a unified architecture. Initially, the Synthetic Minority Oversampling Technique (SMOTE) is applied to address class imbalance in the dataset, followed by constructing a Decision Tree model to extract clear and interpretable decision rules. These rules are then transformed into fuzzy logic rules within the Fuzzy Inference System to handle uncertainty and borderline cases more effectively. Finally, a hard voting mechanism is employed to combine the outputs of both models and produce a robust final decision. The proposed model was evaluated using real-world financial transaction data. Experimental results demonstrated an accuracy of 96.75% ± 0.12, outperforming several baseline models such as Random Forest, CNN-SVM, and rule-based systems. The findings indicate that the proposed hybrid framework enhances detection accuracy, reduces false alarms, and improves interpretability, making it a reliable solution for real-time financial fraud detection.
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