Arseam

International Journal of E Government & E Business Research

AI-BASED GST FRAUD DETECTION SYSTEM FOR LARGE-SCALE E-COMMERCE PLATFORMS

Dr.Suryanarayana N R, Tanuja B S

Dr.Suryanarayana N R, Research Guide & Co-ordinator, HOD, Department of Commerce and Management, ISBR college, electronic City, Bangalore, Karnataka, India-560100

Tanuja B S, IZEE Business School, Jigini, Bengaluru, Karnataka India-560105, India

DOI : https://doi.org/10.5281/zenodo.15273511 Page No : 01-06

Published Online : 2025-04-24

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The rapid growth of e-commerce platforms has multiplied shall we say the fraudulent activities related to Goods and Service Tax (GST). Due to large scale as well as evolving fraud patterns, traditional fraud detection mechanisms are unable to scale and effectively detect fraud. To identify such hidden relationships between entities (sellers, transactions and their tax records), this research presents an AI based GST fraud detection system employing Graph Neural Networks (GNNs). The system uses Google Cloud AI to deploy the system on the scale to handle processing of huge amounts of transactional data, spotting anomalies, and alerting on potential fraud real time. The proposed solution increases fraud detection accuracy while decreasing false positives using different patterns of tax evasion. We conduct experiments for large scale e-commerce ecosystem and our approach is found to be effective in identifying fraudulent activities in such scenarios.

Keywords: GST Fraud Detection, Graph Neural Networks (GNNs), E-Commerce Security, Anomaly Detection, Google Cloud AI, Large-Scale Tax Compliance

REFERENCES

Y. Jain, N. Tiwari, S. Dubey, and S. Jain, "A Comparative Analysis of Various Credit Card Fraud Detection Techniques," International Journal of Recent Technology and Engineering, vol. 7, no. 5S2, pp. 402–407, 2019.
V. N. Dornadula and S. Geetha, "Credit Card Fraud Detection Using Machine Learning Algorithms," in Proceedings of the International Conference on Recent Trends in Advanced Computing (ICRTAC), Procedia Computer Science, vol. 165, pp. 631–641, 2019.
V. D. Sahayasakila, A. Sikhakolli, and V. Yasaswi, "Credit Card Fraud Detection System Using SMOTE Technique and Whale Optimization Algorithm," International Journal of Engineering and Advanced Technology (IJEAT), vol. 8, no. 5, 2019.
S. Khatri, A. Arora, and A. P. Agrawal, "Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison," in Proceedings of the 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, pp. 680–683, 2020.
M. Seera, C. P. Lim, and A. Kumar, "An Intelligent Payment Card Fraud Detection System," Annals of Operations Research, 2021.
E. Ileberi, Y. Sun, and Z. Wang, "A Machine Learning Based Credit Card Fraud Detection Using the GA Algorithm for Feature Selection," Journal of Big Data, vol. 9, no. 24, 2022.
P. Raghavan and N. E. Gayar, "Fraud Detection Using Machine Learning and Deep Learning," in Proceedings of the International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, pp. 334–339, 2019.
N. Verma, K. Uboveja, and M. K. Singh, "Machine Learning Based Fraud Detection for E-Commerce," International Journal of Futuristic Innovation in Engineering, Science and Technology (IJFIEST), vol. 2, no. 1, pp. 4–7, 2023.
G. T. Gangalakshmi, V. Sathya, S. Vinodhini, E. Chandralekha, V. Usha, and S. Ravikumar, "Improving E-commerce Fraud Detection via Machine Learning: Comparative Evaluation of Model Effectiveness," in Proceedings of the 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 2023.
K. M. Ting, "Confusion Matrix," in Encyclopedia of Machine Learning and Data Mining, pp. 260–260, 2017.
M. Suganuma, S. Shirakawa, and T. Nagao, "A Genetic Programming Approach to Designing Convolutional Neural Network Architectures," in Proceedings of the Genetic and Evolutionary Computation Conference, 2017.
S. Ghosh and D. L. Reilly, "Credit Card Fraud Detection with a Neural Network," in Proceedings of the System Sciences Conference, pp. 621–630, 1994.
P. A. Harrison et al., "Selecting Methods for Ecosystem Service Assessment: A Decision Tree Approach," Ecosystem Services, vol. 29, pp. 481–498, 2018.
K. Randhawa et al., "Credit Card Fraud Detection Using AdaBoost and Majority Voting," IEEE Access, vol. 6, pp. 14277–14284, 2018.
T. Li et al., "Differentially Private Naïve Bayes Learning Over Multiple Data Sources," Information Sciences, vol. 444, pp. 89–104, 2018.

Suryanarayana N R, & Tanuja B S (2025), “AI-Based GST Fraud Detection System for Large-Scale E-Commerce Platforms”, International Journal of E-Government & E-Business Research, Vol. 10, Issue 1, 2025, pp 01-06, DOI: https://doi.org/10.5281/zenodo.15273511

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REFERENCES

Y. Jain, N. Tiwari, S. Dubey, and S. Jain, "A Comparative Analysis of Various Credit Card Fraud Detection Techniques," International Journal of Recent Technology and Engineering, vol. 7, no. 5S2, pp. 402–407, 2019.
V. N. Dornadula and S. Geetha, "Credit Card Fraud Detection Using Machine Learning Algorithms," in Proceedings of the International Conference on Recent Trends in Advanced Computing (ICRTAC), Procedia Computer Science, vol. 165, pp. 631–641, 2019.
V. D. Sahayasakila, A. Sikhakolli, and V. Yasaswi, "Credit Card Fraud Detection System Using SMOTE Technique and Whale Optimization Algorithm," International Journal of Engineering and Advanced Technology (IJEAT), vol. 8, no. 5, 2019.
S. Khatri, A. Arora, and A. P. Agrawal, "Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison," in Proceedings of the 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, pp. 680–683, 2020.
M. Seera, C. P. Lim, and A. Kumar, "An Intelligent Payment Card Fraud Detection System," Annals of Operations Research, 2021.
E. Ileberi, Y. Sun, and Z. Wang, "A Machine Learning Based Credit Card Fraud Detection Using the GA Algorithm for Feature Selection," Journal of Big Data, vol. 9, no. 24, 2022.
P. Raghavan and N. E. Gayar, "Fraud Detection Using Machine Learning and Deep Learning," in Proceedings of the International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), Dubai, United Arab Emirates, pp. 334–339, 2019.
N. Verma, K. Uboveja, and M. K. Singh, "Machine Learning Based Fraud Detection for E-Commerce," International Journal of Futuristic Innovation in Engineering, Science and Technology (IJFIEST), vol. 2, no. 1, pp. 4–7, 2023.
G. T. Gangalakshmi, V. Sathya, S. Vinodhini, E. Chandralekha, V. Usha, and S. Ravikumar, "Improving E-commerce Fraud Detection via Machine Learning: Comparative Evaluation of Model Effectiveness," in Proceedings of the 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 2023.
K. M. Ting, "Confusion Matrix," in Encyclopedia of Machine Learning and Data Mining, pp. 260–260, 2017.
M. Suganuma, S. Shirakawa, and T. Nagao, "A Genetic Programming Approach to Designing Convolutional Neural Network Architectures," in Proceedings of the Genetic and Evolutionary Computation Conference, 2017.
S. Ghosh and D. L. Reilly, "Credit Card Fraud Detection with a Neural Network," in Proceedings of the System Sciences Conference, pp. 621–630, 1994.
P. A. Harrison et al., "Selecting Methods for Ecosystem Service Assessment: A Decision Tree Approach," Ecosystem Services, vol. 29, pp. 481–498, 2018.
K. Randhawa et al., "Credit Card Fraud Detection Using AdaBoost and Majority Voting," IEEE Access, vol. 6, pp. 14277–14284, 2018.
T. Li et al., "Differentially Private Naïve Bayes Learning Over Multiple Data Sources," Information Sciences, vol. 444, pp. 89–104, 2018.

Int. J. of E-Gov. & E-Busi Res

AI-BASED GST FRAUD DETECTION SYSTEM FOR LARGE-SCALE E-COMMERCE PLATFORMS

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