Transparent Peer Review By Scholar9
RULE-BASED SYSTEMS AND ENSEMBLE LEARNING TECHNIQUES FOR DIGITAL FRAUD DETECTION
Abstract
Fraud has been rather pervasive and very costly in this interwoven world, posing a threat to the stability and reliability of various industries. With such a worrying surge of sophisticated fraud techniques and ever-evolving fraudulent patterns in behaviors, it goes without saying that some new, adaptive solution for fraud detection is in order. Artificial intelligence can prove really powerful in fighting fraud and can turn out to be very promising in raising the efficiency and accuracy of the detection systems. This paper presents new ways of dealing with fraud and introduces techniques of AI-powered fraud detection. It describes the current landscape of fraud detection approaches, covering traditional rule-based techniques and more recent methodologies, first based on statistical methods and then on machine learning techniques. It, therefore, outlines the limitations of such methods and emphasizes the need for AI-driven solutions to help overcome the potential barriers that dynamism in fraud poses. Here, it presents three AI-based techniques for fraud detection: Graph Neural Networks, Generative Adversarial Networks, and Temporal Convolutional Networks. All of them aim to make good use of the strengths found in various AI techniques.
Phanindra Kumar Kankanampati Reviewer
10 Oct 2024 03:47 PM
Not Approved
Relevance and Originality
The Research Article addresses a pressing issue in today’s interconnected world, where fraud significantly threatens industry stability and reliability. Its relevance is underscored by the increasing sophistication of fraud techniques. The originality of the paper lies in its focus on AI-driven solutions, which represent a novel approach to enhancing fraud detection methods, moving beyond traditional techniques and incorporating advanced methodologies.
Methodology
The Research Article outlines various fraud detection approaches, categorizing them into traditional rule-based techniques, statistical methods, and machine learning methodologies. However, it lacks detailed methodological frameworks for implementing the AI-based techniques discussed, such as Graph Neural Networks, Generative Adversarial Networks, and Temporal Convolutional Networks. Including specific methodologies, data sources, and experimental setups would strengthen the research’s robustness.
Validity & Reliability
The validity of the AI-powered techniques presented relies on their ability to effectively address the limitations of existing methods. While the Research Article highlights these limitations, empirical evidence supporting the efficacy of the AI techniques would enhance its credibility. Data on the performance of these techniques in real-world scenarios would provide a clearer understanding of their reliability and effectiveness.
Clarity and Structure
The Research Article is well-organized, providing a clear overview of the current fraud detection landscape. However, the clarity could be improved by using more subheadings and visual aids, such as diagrams or flowcharts, to illustrate the differences between traditional and AI-based approaches. This would help readers quickly grasp complex concepts and improve overall engagement with the content.
Result Analysis
The result analysis section emphasizes the need for AI-driven solutions in fraud detection, but it lacks quantitative data showcasing the success of the proposed techniques. Including metrics on detection rates, false positives, and efficiency improvements would substantiate the claims made. Additionally, discussing future research directions and potential challenges in implementing these AI techniques would enrich the analysis and provide a comprehensive understanding of the topic.
IJ Publication Publisher
Done Sir
Phanindra Kumar Kankanampati Reviewer