EVALUATING THE PERFORMANCE OF DEEP LEARNING MODELS FOR CREDIT CARD FRAUD DETECTION

Abstract:

Credit card fraud has become a prevalent issue in the digital era, posing significant financial losses for individuals and organizations alike. Traditional rule-based fraud detection methods are often insufficient to combat the sophisticated and evolving nature of fraudulent activities. As a result, deep learning models have emerged as promising tools for credit card fraud detection due to their ability to automatically learn complex patterns and adapt to new fraud schemes.

This paper presents an evaluation of the performance of deep learning models for credit card fraud detection. The study focuses on assessing the effectiveness of various deep learning architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid models, in accurately identifying fraudulent transactions while minimizing false positives.

The evaluation utilizes a publicly available credit card fraud dataset, which consists of legitimate and fraudulent transactions, to train and test the deep learning models. Several performance metrics, including accuracy, precision, recall, and F1-score, are employed to evaluate the models’ effectiveness in distinguishing between genuine and fraudulent transactions.

Furthermore, the study explores the impact of different factors, such as dataset imbalance, feature engineering, and model hyperparameters, on the performance of deep learning models. Techniques such as oversampling, undersampling, and synthetic minority oversampling technique (SMOTE) are employed to address the class imbalance issue commonly encountered in credit card fraud detection datasets.

The experimental results demonstrate that deep learning models, particularly hybrid architectures combining CNNs and RNNs, exhibit superior performance compared to traditional methods and standalone neural network models. The models achieve high accuracy, precision, recall, and F1-score, thus demonstrating their potential for effective credit card fraud detection.

In conclusion, this study provides a comprehensive evaluation of deep learning models for credit card fraud detection. The findings highlight the strengths and limitations of different architectures and shed light on the factors influencing their performance. The results contribute to the existing body of research in this field and provide valuable insights for developing robust and accurate fraud detection systems to mitigate financial losses and protect consumers.

EVALUATING THE PERFORMANCE OF DEEP LEARNING MODELS FOR CREDIT CARD FRAUD DETECTION, GET MORE MASTERS COMPUTER SCIENCE

Sharing is caring!