Abstract

Fraud detection has been at the forefront of technology and research for decades, focusing on the right techniques and tools for accurate detection in a digital banking system. With the introduction of big data in various domains, its technologies and tools lend eyes and ears to businesses, ensuring rapid and accurate decision-making. The financial domain has evolved immensely with the introduction of cards, mobile banking, and internet banking; security has also become crucial for banks and their customers. Fraud detection and prevention in the banking environment is a costly affair, leading to financial losses for banking sectors, retailers, and the customers themselves. Members of the fraud detection and prevention team work under increasing pressure to act blazily and investigate major frauds. To meet these demands, state-of-the-art and new technologies have been explored. The use of big data analytics techniques and algorithms on the can detect fraud effectively and help in fraud prevention. When applied to a banking environment, red-flags are generated in an ethical manner, providing the necessary information to the fraud detection team so they can act decisively regarding standing transactions. The use of Big Data Analytics can help detect fraud early in the banking process.

Keywords

  • Big Data
  • Data Mining
  • Big Data Mining
  • Data Security
  • Security Activities
  • Cloud Environment
  • Banki

References

  1. Varri, D. B. S. (2020). Automated Vulnerability Detection and Remediation Framework for Enterprise Databases. Available at SSRN 5774865.
  2. Asha, R. B., & Suresh Kumar, K. R. Credit card fraud detection using artificial neural network. Global Transitions Proceedings, 2(1), 35–41.
  3. Rongali, S. K. (2020). Predictive Modeling and Machine Learning Frameworks for Early Disease Detection in Healthcare Data Systems. Current Research in Public Health, 1(1), 1–15. Retrieved from https://www.scipublications.com/journal/index.php/crph/article/view/1355s
  4. Bin Sulaiman, R., Schetinin, V., & Sant, P. Review of machine learning approach on credit card fraud detection. Human-Centric Intelligent Systems, 2(1–2), 55–68.
  5. Integrating Big Data and AI in Cloud-Based Healthcare Systems for Enhanced Patient Care and Disease Management. (2020). Global Research Development(GRD) ISSN: 2455-5703, 5(12), 19-42. https://doi.org/10.70179/g32nmm07.
  6. Chang, V., Doan, L. M. T., Di Stefano, A., Sun, Z., & Fortino, G. Digital payment fraud detection methods in digital ages and Industry 4.0. Computers & Electrical Engineering, 100, 107734.
  7. Annapareddy, V. N. (2020). Integrating Solar Infrastructure with Cloud Computing for Scalable Energy Solutions. Global Research Development (GRD) ISSN: 2455-5703, 5(12), 152-170.
  8. Dou, Y., Liu, Z., Sun, L., Deng, Y., Peng, H., & Yu, P. S. (2020). Enhancing graph neural network-based fraud detectors against camouflaged fraudsters (CARE-GNN). In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM) (pp. 315–324). ACM.
  9. Chakilam, C., Koppolu, H. K. R., Chava, K. C., & Suura, S. R. (2020). Integrating Big Data and AI in Cloud-Based Healthcare Systems for Enhanced Patient Care and Disease Management. Global Research Development (GRD) ISSN: 2455-5703, 5(12), 19-42.
  10. Jullum, M., Løland, A., Huseby, R. B., Ånonsen, G., & Lorentzen, J. (2020). Detecting money laundering transactions with machine learning. Journal of Money Laundering Control, 23(1), 173–186.
  11. Nuka, S. T. (2020). Predictive Modeling in Healthcare: Early Diagnosis and Patient Risk Profiling Using Machine Learning. Global Research Development (GRD) ISSN: 2455-5703, 5(12), 96-115.
  12. Khan, S., Alourani, A., Mishra, B., Ali, A., & Kamal, M. Developing a credit card fraud detection model using machine learning approaches. International Journal of Advanced Computer Science and Applications, 13(3), 411–418.
  13. Dwaraka Nath Kummari, Srinivasa Rao Challa, "Big Data and Machine Learning in Fraud Detection for Public Sector Financial Systems," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2020.91221.
  14. Madhavi, A., & Sivaramireddy, T. Real-time credit card fraud detection using Spark framework. In Machine Learning Technologies and Applications: Proceedings of ICACECS 2020 (pp. 287–298). Springer.
  15. Mohan, K., & coauthors. (2020). Deep temporal embeddings for cross-border money laundering detection. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM) (pp. 2351–2360). ACM.
  16. Burugulla, J. K. R. (2020). The Role of Cloud Computing in Scaling Secure Payment Infrastructures for Digital Finance. Global Research Development (GRD) ISSN: 2455-5703, 5(12).
  17. Pamisetty, V. (2020). Optimizing Unclaimed Property Management through Cloud-Enabled AI and Integrated IT Infrastructures. Universal Journal of Finance and Economics, 1(1), 1–20. Retrieved from https://www.scipublications.com/journal/index.php/ujfe/article/view/1338.
  18. Patil, S., Nemade, V., & Soni, P. K. (2018). Predictive modelling for credit card fraud detection using data analytics. Procedia Computer Science, 132, 385–395.
  19. Pei, Y., & coauthors. (2020). Subgraph anomaly detection in financial transaction networks. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management (CIKM) (pp. xxx–xxx). ACM.
  20. Pamisetty, A. (2019). Big Data Engineering for Real-Time Inventory Optimization in Wholesale Distribution Networks. Available at SSRN 5267328.
  21. Santosh, T., & Dharavath, R. (2020). Machine learning approach on Apache Spark for credit card fraud detection. Ingénierie des Systèmes d’Information, 25(1), 101–106.
  22. Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
  23. Yeşilkanat, A. (2020). An adaptive approach on credit card fraud detection using sliding-window retraining to handle concept drift. Applied Sciences, 10(x), xxxx.
  24. Gottimukkala, V. R. R. (2020). Energy-Efficient Design Patterns for Large-Scale Banking Applications Deployed on AWS Cloud. power, 9(12).