Abstract

Real-time analysis of data in-database and often in-stream, rather than by pulling it out into a data warehouse or data lake and then applying model inference, is emerging in several application domains, primarily driven by the low-latency constraints of use cases such as fraud detection in financial systems and cyber-security. When transaction processing systems detect patterns of user behavior that appear suspicious, they typically take a do-not-honor strategy and decline the transaction. Such patterns, unless they are simple signature patterns that can be identified with very small false-positive rates, are hard to model during training because fraudulent activity is highly imbalanced compared to non-fraudulent activity. Therefore, anomaly-detection solutions that look toward the tail of the distribution space have a better chance of success. A hallmark feature of modern financial transaction systems is the ability to analyze a transaction before honoring it and thereby also potentially preventing a fraud. Fraud detection in that real-time operational manner differs from cold-start fraud detection, where fraud detection is a batch analysis with limited decision support for the fraudsters. In operational fraud detection, the decision support provided makes the transaction failure prediction a mark of caution and the user behavior requires examination, since a genuine user behavior may be wrongly identified as a fraud attack.

Keywords

  • Fraud detection
  • artificial intelligence
  • payment transactions
  • texts and images
  • real-time processi

References

  1. Guntupalli, R. AI-Driven Threat Detection and Mitigation in Cloud Infrastructure: Enhancing Security through Machine Learning and Anomaly Detection. Available at SSRN.
  2. Chatterjee, P., Das, D., & Rawat, D. B. Digital twin for credit card fraud detection: Opportunities, challenges, and fraud detection advancements. Future Generation Computer Systems. doi: ./.future.
  3. Sateesh Kumar Rongali. Explainable Artificial Intelligence (XAI) Framework for Transparent Clinical Decision Support Systems. International Journal of Medical Toxicology and Legal Medicine. Retrieved from https://ijmtlm.org/index.php/journal/article/view/.
  4. Cherif, A., Badhib, A., Ammar, H., Alshehri, S., Kalkatawi, M., & Imine, A. Credit card fraud detection in the era of disruptive technologies: A systematic review. Journal of King Saud University – Computer and Information Sciences. doi: ./.jksuci.
  5. Varri, D. B. S. Advanced Threat Intelligence Modeling for Proactive Cyber Defense Systems. Available at SSRN.
  6. Cheng, D., Wang, X., Zhang, Y., & Zhang, L. Graph neural network for fraud detection via spatial–temporal attention. IEEE Transactions on Knowledge and Data Engineering. doi:./TKDE..
  7. Inala, R. Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective.
  8. Ileberi, E., Sun, Y., & Wang, Z. A machine learning based credit card fraud detection using the GA algorithm for feature selection. Journal of Big Data. doi:./s--.
  9. Garapati, R. S. Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
  10. Mienye, I. D., & Sun, Y. A deep learning ensemble with data resampling for credit card fraud detection. IEEE Access. doi:./ACCESS..
  11. Nagabhyru, K. C. From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN.
  12. Motie, S., & Raahemi, B.Financial fraud detection using graph neural networks: A systematic review. Expert Systems with Applications. doi: ./.eswa.
  13. Aitha, A. R. CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List.
  14. Rao, B., & Wang, L. Streaming GNNs for fast financial event detection. IEEE Transactions on Big Data. Advance online publication. doi:./TBDATA..
  15. Keerthi Amistapuram. Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice. https://doi.org//kuey.v.i.
  16. Sergadeeva, A. I., Lavrova, D. S., & Zegzhda, D. P. Bank fraud detection with graph neural networks. Automatic Control and Computer Sciences. doi:./S
  17. Nagubandi, A. R. Advanced Multi-Agent AI Systems for Autonomous Reconciliation Across Enterprise Multi-Counterparty Derivatives, Collateral, and Accounting Platforms. International Journal of Finance (IJFIN)-ABDC Journal Quality List.
  18. Wang, Y., Zhan, H., & Jiang, W. Time encoding graph attention model for financial fraud detection in large-scale financial social networks. In Proceedings of the International Conference on Cryptography, Network Security and Communication Technology (CNSCT) (pp. –). Association for Computing Machinery. doi:./.
  19. Gottimukkala, V. R. R. Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List.
  20. Xiao, F., Wu, Y., Zhang, M., Chen, G., & Ooi, B. C. MINT: Detecting fraudulent behaviors from time-series relational data. Proceedings of the VLDB Endowment. doi:./.
  21. Gadi, A. L. The Role Of AI-Driven Predictive Analytics In Automotive R&D: Enhancing Vehicle Performance And Safety.
  22. Zhu, X., et al. Graph neural networks: A review of methods and applications. AI Open. doi: ./.aiopen..
  23. Pandiri, L. Leveraging AI and Machine Learning for Dynamic Risk Assessment in Auto and Property Insurance Markets. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI.
  24. Pourhabibi, T., Ong, K. L., Kam, B. H., & Boo, Y. H. Fraud detection: A systematic literature review of graph-based anomaly detection approaches. Decision Support Systems. doi: ./.dss..
  25. Recharla, M., & Chitta, S. AI-Enhanced Neuroimaging and Deep Learning-Based Early Diagnosis of Multiple Sclerosis and Alzheimer’s.
  26. West, J., & Bhattacharya, M. Intelligent financial fraud detection: A comprehensive review. Computers & Security. doi: ./.cose..
  27. Nandan, B. P., & Chitta, S. S. Machine Learning Driven Metrology and Defect Detection in Extreme Ultraviolet (EUV) Lithography: A Paradigm Shift in Semiconductor Manufacturing. Educational Administration: Theory and Practice.