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Original Article
Enhancing Financial Fraud Detection by Leveraging Llama2 NLP and Neo4j
Shaik Mohammad Adil1
Dr. Mohd Rafi Ahmed2
1Student, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India. 2Associate Professor, MCA Deccan College of Engineering and Technology, Hyderabad, Telangana, India.
Published Online: September-December 2025
Pages: 39-44
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403008
References
1. S. Gupta and R. Singh, “Machine learning techniques for fraud detection in digital banking: A comprehensive review,” IEEE Access, vol. 7, pp. 102–118, 2019.
2. H. Li, Y. Zhao, and J. Wang, “Adaptive anomaly detection in financial systems using ensemble learning,” IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 10, pp. 3812–3825, Oct. 2019.
3. P. Zhang, Q. Liu, and K. Chen, “Real-time fraud detection for electronic payments using graph-based methods,” IEEE Trans. Knowl. Data Eng., vol. 32, no. 8, pp. 1532–1545, Aug. 2020.
4. R. Kumar and A. Sharma, “Deep neural networks for financial fraud detection: Challenges and opportunities,” IEEE Access, vol. 8, pp. 67523–67535, 2020.
5. Y. Luo, F. Wu, and T. Huang, “Context-aware fraud detection in fintech applications using NLP and graph modeling,” IEEE Trans. Ind. Informat., vol. 16, no. 12, pp. 7620–7630, Dec. 2020.
6. J. Chen and D. Wu, “Application of graph neural networks for anti-money laundering detection,” IEEE Int. Conf. Big Data (BigData), pp. 2154–2163, 2020.
7. S. Patel and P. Jain, “Natural language processing for fraud investigation: A banking perspective,” IEEE Access, vol. 9, pp. 12102–12115, 2021.
8. T. Nguyen, M. Hoang, and D. Tran, “Neo4j-based fraud detection system for relational transaction analysis,” IEEE Int. Conf. Data Mining (ICDM), pp. 135–144, 2021.
9. L. Wang, J. Liu, and M. Zhang, “Fraud detection using graph databases and machine learning algorithms,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 6, pp. 1255–1266, Jun. 2020.
10. M. Patel, R. Gupta, and A. Sharma, “Leveraging LSTM and graph analytics for financial fraud detection in real-time systems,” IEEE Access, vol. 9, pp. 14503–14515, 2021.
2. H. Li, Y. Zhao, and J. Wang, “Adaptive anomaly detection in financial systems using ensemble learning,” IEEE Trans. Neural Netw. Learn. Syst., vol. 31, no. 10, pp. 3812–3825, Oct. 2019.
3. P. Zhang, Q. Liu, and K. Chen, “Real-time fraud detection for electronic payments using graph-based methods,” IEEE Trans. Knowl. Data Eng., vol. 32, no. 8, pp. 1532–1545, Aug. 2020.
4. R. Kumar and A. Sharma, “Deep neural networks for financial fraud detection: Challenges and opportunities,” IEEE Access, vol. 8, pp. 67523–67535, 2020.
5. Y. Luo, F. Wu, and T. Huang, “Context-aware fraud detection in fintech applications using NLP and graph modeling,” IEEE Trans. Ind. Informat., vol. 16, no. 12, pp. 7620–7630, Dec. 2020.
6. J. Chen and D. Wu, “Application of graph neural networks for anti-money laundering detection,” IEEE Int. Conf. Big Data (BigData), pp. 2154–2163, 2020.
7. S. Patel and P. Jain, “Natural language processing for fraud investigation: A banking perspective,” IEEE Access, vol. 9, pp. 12102–12115, 2021.
8. T. Nguyen, M. Hoang, and D. Tran, “Neo4j-based fraud detection system for relational transaction analysis,” IEEE Int. Conf. Data Mining (ICDM), pp. 135–144, 2021.
9. L. Wang, J. Liu, and M. Zhang, “Fraud detection using graph databases and machine learning algorithms,” IEEE Trans. Comput. Soc. Syst., vol. 7, no. 6, pp. 1255–1266, Jun. 2020.
10. M. Patel, R. Gupta, and A. Sharma, “Leveraging LSTM and graph analytics for financial fraud detection in real-time systems,” IEEE Access, vol. 9, pp. 14503–14515, 2021.
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