OptDevNet: A Optimized Deep Event-Based Network Framework for Credit Card Fraud Detection

OptDevNet: A Optimized Deep Event-Based Network Framework for Credit Card Fraud Detection

Blog Article

In recent times, credit card fraud has Occupational emerged as a substantial financial challenge for both cardholders and the issuing authorities.To address this demanding issue, researchers have employed machine learning techniques to identify fraudulent activities within labeled transaction records.However, these techniques have primarily been evaluated on limited or specific datasets, which may not adequately represent the broader real-world scenario.These limitations motivated us to comprehensively assess the existing machine learning classifiers and propose an Optimized Deep Event-based Network (OptDevNet) framework capable of addressing these challenges.To evaluate the performance of the proposed model, we implemented and assessed five different machine learning classifiers using the well-known Credit Card Fraud Detection (CCFD) Dataset.

Upon careful analysis, we found that our model surpasses these classifiers in terms of fraudulent transaction detection accuracy.Given these Steamer Handle findings, we are confident that our proposed model has the potential for effective real-world deployment in detecting and preventing malicious transactions.