Transparent Peer Review By Scholar9
Credit Card Fraud Detection Using State-of-the-Art Machine Learning and Deep Learning Algorithms
Abstract
People could use credit cards to buy things online because they are handy and easy to use. As more people use credit cards, more people abuse them too. People who use stolen credit cards lose a lot of money, and so do banks and other financial institutions. The main goal of this research study is to find these frauds, such as those with a lot of false alarms, data that is open to the public, data that shows a big difference in class, and changes in the type of scam. There are several credit card recognition methods based on machine learning that have been written about. Think about the Extreme Learning Method, SVM, Random Forest, Decision Tree, XG Boost, and Logistic Regression. State-of-the-art deep learning algorithms are as yet expected to limit fake consumptions in light of their poor accuracy. Utilizing the latest deep learning strategies has been the objective. Machine learning and deep learning theories were differentiated to come by great results. The whole scientific stealing research uses the European Card Benchmark sample. First, the information was put through a machine learning method, which helped find frauds to some degree. In the end, three designs based on convolutional neural networks are used to make scam detection work better. By adding more levels, the accuracy of recognition went up by a large amount. A full observational study was carried out using the most up-to-date models and changing the number of secret layers and epochs. By looking at the study work, we can see that the results got better. The accuracy went up to 99.9%, the f1-score went up to 85.71%, the precision went up to 98%, and the AUC curves had ideal values of 93%, 98%, 85.71%, and 99.9%. The suggested model does a better job of recognizing credit cards than modern machine learning and deep learning methods. We also tried using deep learning and adjusting the data to get the false negative rate down. There are good ways to spot credit card theft in the real world that are being shown.
Priyank Mohan Reviewer
15 Oct 2024 12:42 PM
Approved
Relevance and Originality
The study addresses a critical issue in the realm of finance and cybersecurity: credit card fraud detection. Given the widespread use of credit cards and the increasing prevalence of fraudulent activities, this research is highly relevant and timely. The originality of the study lies in its exploration of multiple machine learning techniques, including deep learning approaches, to enhance fraud detection accuracy. By integrating various methods and using the European Card Benchmark sample, the study contributes novel insights into the effectiveness of these algorithms in real-world scenarios.
Methodology
The methodology employed in this research is sound, featuring a mix of traditional machine learning algorithms (such as SVM, Random Forest, and Logistic Regression) alongside advanced deep learning architectures, particularly convolutional neural networks (CNNs). The stepwise approach—beginning with conventional methods and progressing to more complex models—demonstrates a comprehensive understanding of the fraud detection landscape. However, the article could benefit from a clearer explanation of the experimental setup, including how the dataset was preprocessed, the rationale for selecting specific algorithms, and the parameter tuning process for deep learning models.
Validity & Reliability
The validity of the findings is bolstered by the impressive results achieved with the proposed models, particularly the reported accuracy of 99.9% and the corresponding precision and F1 scores. These metrics suggest a strong performance in distinguishing between legitimate and fraudulent transactions. However, to enhance reliability, the study should include details on the validation process, such as cross-validation techniques or test/train splits, to ensure that the results are not due to overfitting. Additionally, discussing potential biases in the dataset or limitations in the model performance would provide a more balanced perspective.
Clarity and Structure
The article is generally well-structured, moving logically from the introduction of the problem to the methodologies employed and the results obtained. However, the writing could be clearer in certain areas, particularly when describing complex algorithms and methodologies. Including more visual aids, such as diagrams of the model architectures or flowcharts of the fraud detection process, could enhance comprehension. Additionally, a brief overview of key terminology (e.g., explaining terms like "AUC" and "false negative rate") would make the article more accessible to a broader audience.
Result Analysis
The result analysis presents compelling evidence of the effectiveness of the proposed model in credit card fraud detection. The reported metrics, such as the accuracy and precision values, underscore the model's robustness compared to traditional methods. However, the analysis could be enriched by providing comparative performance data against baseline models or existing literature. Discussing the implications of achieving such high accuracy—particularly in terms of potential impacts on false positives and operational costs for financial institutions—would also add depth to the findings. Furthermore, exploring the practical applications of the model in real-world scenarios, including any limitations or challenges faced in deployment, would provide valuable insights for practitioners in the field.
IJ Publication Publisher
done sir
Priyank Mohan Reviewer