Transparent Peer Review By Scholar9
DETECTING GST FRAUD THROUGH MACHINE LEARNING TECHNIQUES
Abstract
The Goods and Services Tax (GST) is a significant change in tax policy that is designed to improve transparency and efficiency. Nevertheless, GST fraud continues to be a significant concern, obstructing tax compliance and revenue collection. The objective of this paper is to improve the integrity of the tax system by introducing a machine learning-based approach to the detection of GST fraud. A survey of 50 participants, including tax professionals and software engineers, is included in the methodology to collect insights on prevalent fraud tactics and challenges. Data on typical fraudulent behaviours and extant detection methods was collected through the use of a questionnaire. For the purpose to analyse transaction data and identify anomalies that suggest fraud, machine learning techniques were implemented. In order to detect anomalies, a variety of algorithms were implemented, including supervised methods such as decision trees and random forests, as well as unsupervised methods like clustering. The models were trained and evaluated using historical transaction records in conjunction with survey data. The results indicate that traditional methods are considerably outperformed by machine learning models in terms of the detection of deceptive activities. High accuracy was demonstrated in the identification of patterns associated with tax evasion, including fraudulent invoices and unreported transactions, by specific algorithms, such as random forests. The significance of the integration of advanced detection systems and continuous model updates into tax administration was also underscored by the results. In summary, machine learning is a potent instrument for the detection of GST fraud, providing improved efficiency and accuracy. The integration of these technologies into tax compliance frameworks can result in more effective fraud prevention and revenue assurance, which is advantageous for both tax authorities and businesses.
Shreyas Mahimkar Reviewer
Hi sir
Shreyas Mahimkar Reviewer
23 Aug 2024 04:16 PM
Approved
Relevance and Originality:
Positive: The topic of GST fraud detection is highly relevant in the current economic climate, where tax compliance is critical for revenue assurance. The application of machine learning to enhance fraud detection represents a novel approach, contributing original insights to the field.
Negative: While the approach is innovative, the abstract does not clearly specify how it advances beyond existing literature on machine learning applications in tax fraud detection. More emphasis on the uniqueness of the methodology or findings would strengthen the originality.
Methodology:
Positive: The combination of supervised and unsupervised machine learning techniques provides a comprehensive analysis of fraudulent activities. The use of both historical transaction data and survey responses adds depth to the methodology, ensuring a well-rounded approach.
Negative: The abstract mentions a survey of 50 participants, but it does not clarify the selection criteria or the representativeness of the sample. Additionally, the methodology lacks detail on the specific parameters used in the machine learning models, which is crucial for assessing the robustness of the approach.
Validity & Reliability:
Positive: The use of historical transaction data to train and evaluate models lends credibility to the findings. The high accuracy of specific algorithms, like random forests, indicates reliable results in detecting patterns of tax evasion.
Negative: The reliability of the survey data could be questioned, especially if the sample size is small or not representative of the broader population. Moreover, the abstract does not discuss the potential limitations or biases inherent in the machine learning models, which could impact the validity of the conclusions.
Clarity and Structure:
Positive: The abstract is well-structured, providing a clear progression from the problem statement to the methodology, results, and conclusions. The language is concise and accessible, making it easy for readers to understand the key points.
Negative: Some aspects of the methodology, particularly the survey and machine learning techniques, could be elaborated for better clarity. The abstract could benefit from a brief mention of the practical implications or recommendations for future research.
Results and Analysis:
Positive: The results indicate that machine learning models significantly outperform traditional methods in detecting GST fraud, which is a strong and valuable finding. The specific mention of high accuracy in identifying fraudulent patterns highlights the effectiveness of the proposed approach.
Negative: The abstract does not provide specific metrics (e.g., accuracy percentages, precision, recall) to quantify the performance of the models. Additionally, the analysis could be enriched by discussing the challenges faced during the implementation of these models and how they were addressed.
IJ Publication Publisher
Thank you
Shreyas Mahimkar Reviewer