Transparent Peer Review By Scholar9
Online Payment Fraud Detection
Abstract
As we are approaching modernity, the trend of paying online is increasing tremendously. It is very beneficial for the buyer to pay online as it saves time, and solves the problem of free money. Also, we do not need to carry cash with us. But we all know that Good thing are accompanied by bad things. The online payment method leads to fraud that can happen using any payment app. That is why Online Payment Fraud Detection is very important. In this we have use two algorithm it is Support Vector Classification (SVC) and Logistic Regression which has accuracy of 100% and 50% respectively . By using this it will become easy to identify suspicious patterns and behaviors that may indicate fraudulent activity .
Sivaprasad Nadukuru Reviewer
04 Oct 2024 02:32 PM
Approved
Relevance and Originality
The topic of online payment fraud detection is highly relevant in today's digital economy, where online transactions are increasingly common. The exploration of this issue is original, particularly given the rapid growth of e-commerce and the corresponding rise in cyber threats. The focus on fraud detection algorithms, specifically Support Vector Classification (SVC) and Logistic Regression, provides valuable insights for businesses and researchers looking to enhance security measures in online payments.
Methodology
The methodology outlined in the paper appears straightforward, focusing on the implementation of two specific algorithms for fraud detection. However, the paper would benefit from a more detailed description of the data used for training and testing these algorithms, including how the dataset was selected and preprocessed. Additionally, discussing the criteria for evaluating the algorithms beyond accuracy (e.g., precision, recall, F1 score) would enhance the methodological rigor and provide a more comprehensive understanding of their effectiveness.
Validity & Reliability
While the reported accuracy rates of 100% for SVC and 50% for Logistic Regression are compelling, the validity and reliability of these findings should be further substantiated. It is important to clarify the context in which these results were achieved, including the dataset size and the specific conditions under which the algorithms were tested. Potential overfitting issues with the SVC model should also be addressed, as well as the implications of such accuracy rates on real-world applications.
Clarity and Structure
The paper presents its ideas clearly, but it would benefit from improved structure. Introducing distinct sections (e.g., Introduction, Methodology, Results, Discussion) would help readers navigate the content more easily. Providing a clearer explanation of terms and concepts, especially for readers less familiar with machine learning, would enhance overall comprehension. Additionally, visual aids such as graphs or tables comparing the performance of the algorithms could improve clarity.
Result Analysis
The analysis of the algorithms' performance is a crucial aspect of the paper. However, further exploration of the implications of these results for practical applications in online payment systems would strengthen the discussion. Including real-world examples of how such detection systems could be implemented or have been successfully utilized would provide concrete context. Additionally, addressing limitations of the algorithms and suggesting areas for future research would offer a more rounded conclusion, helping to frame the ongoing challenges in online payment fraud detection.
IJ Publication Publisher
Thank You Sir
Sivaprasad Nadukuru Reviewer