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.
Hemant Singh Sengar Reviewer
15 Oct 2024 02:09 PM
Approved
Relevance and Originality
The research article addresses a critical issue in the financial sector—credit card fraud—making it highly relevant in today's digital economy. The focus on employing advanced machine learning and deep learning techniques to enhance fraud detection demonstrates originality, especially in the context of increasing online transactions and associated risks. However, the study could further emphasize how it distinguishes itself from existing literature, particularly in terms of the specific methodologies and datasets used, to enhance its originality.
Methodology
The methodology outlines a systematic approach to detect credit card fraud using various machine learning algorithms, including Extreme Learning Method, SVM, Random Forest, Decision Tree, XG Boost, and Logistic Regression. The research utilizes the European Card Benchmark sample, which is appropriate for the study's objectives. However, the article would benefit from more detailed explanations of the data preprocessing steps, feature selection, and model training processes. Additionally, clarity regarding the evaluation metrics used to assess model performance would strengthen the methodology section.
Validity & Reliability
The reported accuracy of 99.9% and other metrics such as F1-score, precision, and AUC curves indicate a high level of performance for the proposed models. However, to enhance the validity of these findings, the study should provide information on the test set size and the methods used to validate results, such as cross-validation or separate validation datasets. Discussing potential limitations and biases in the dataset, as well as the applicability of the results to real-world scenarios, would further support the reliability of the conclusions drawn.
Clarity and Structure
The article is generally well-structured, presenting a logical flow from problem identification to proposed solutions and results. However, clarity can be improved by organizing the content into distinct sections with clear headings, especially in the methodology and results parts. Some technical terms, like "convolutional neural networks," should be briefly explained for readers who may not be familiar with them. Overall, improving readability through careful editing and more organized presentation would enhance the article's effectiveness.
Result Analysis
The analysis of results is comprehensive, showcasing the performance of various models and emphasizing the superiority of the proposed convolutional neural network designs. However, the article could benefit from a more detailed discussion of the implications of these results, such as how they can be implemented in real-world applications or their potential impact on reducing false positives in fraud detection. Additionally, comparing the proposed models' performance against industry standards or existing fraud detection systems would provide further context for the findings. Overall, while the results are promising, a deeper exploration of their significance would strengthen the analysis.
IJ Publication Publisher
thankyou sir
Hemant Singh Sengar Reviewer