AI-Powered Data Engineering Solutions for Real-Time Fraud Prevention in Banking
DOI:
https://doi.org/10.5281/zenodo.20427370Keywords:
Artificial Intelligence, Data Engineering, Real-Time Fraud Detection, Banking Security, Machine Learning, Financial Fraud Prevention, Big Data Analytics, Predictive Analytics,Transaction Monitoring,Intelligent Banking Systems.Abstract
Fraud prevention poses difficult challenges for retail banks, particularly for cross-border and cross-channel transactions where data tend to reside in silos. AI detection models are being attacked, and decision systems are subject to adversarial data. AI-powered data engineering is essential for ingesting, processing, and distributing risk signals with minimal latency, supporting the use of longer windows of historical data and the fusion of signals from multiple products and channels. Timely data quality and availability are determining factors in maintaining detection quality and accuracy. Recently reported detection and mitigation tactics, including model poisoning, retraining data poisoning, and feature value manipulation, should receive close attention in detection system design.
Fraud prevention in retail banking typically involves detecting transactions that do not match a customer’s usual behavior or transactions that are dodgy in nature. The signals for detection usually come from behavioral analytics, anomaly detection, and cross-entity analysis. Such detections of fraud are often an obvious requirement, especially for transfer-based money laundering, but also for other fraud types. With money laundering being a major priority for regulators in many jurisdictions, banks have to conduct automated KYC (know your customer) checks, including for transactions beyond a preset threshold.
References
1. Gottimukkala, V. R. R. (2022). Licensing Innovation in the Financial Messaging Ecosystem: Business Models and Global Compliance Impact. International Journal of Scientific Research and Modern Technology, 1(12), 177-186.
2. Singireddy, S. (2023). Integrating Deep Learning and Machine Learning Algorithms in Insurance Claims Processing: A Study on Enhancing Accuracy, Speed, and Fraud Detection for Policyholders. Educ. Adm. Theory Pract. https://doi. org/10.53555/kuey. v29i4, 9668.
3. Yandamuri, U. S. (2022). Cloud-Based Data Integration Architectures for Scalable Enterprise Analytics. International Journal of Intelligent Systems and Applications in Engineering, 10, 472-483.
4. Kolla, S. H. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications, 31(4).
5. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
6. Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology (IJSRMT).
7. Bindu Madhavi Mangalampalli. Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform. 2022; 11. DOI: 10.30699/fhi.v11i1.388
8. Segireddy, A. R. (2022). Terraform and Ansible in Building Resilient Cloud-Native Payment Architectures. International Journal of Intelligent Systems and Applications in Engineering, 10, 444-455.
9. Amistapuram, K. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29 (4), 5950–5958.
10. Pamisetty, V., Dodda, A., Lakarasu, P., Singireddy, J., & Challa, K. (2022). Optimizing Digital Finance and Regulatory Systems Through Intelligent Automation, Secure Data Architectures, and Advanced Analytical Technologies. Secure Data Architectures, and Advanced Analytical Technologies (December 10, 2022).
11. Kalisetty, S., & Singireddy, J. (2023). Agentic AI in retail: A paradigm shift in autonomous customer interaction and supply chain automation. American Advanced Journal for Emerging Disciplinaries (AAJED) ISSN, 3067-4190.
12. Singireddy, S. (2023). Reinforcement Learning Approaches for Pricing Condo Insurance Policies. American Journal of Analytics and Artificial Intelligence (ajaai) with ISSN.
13. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706.
14. Kolla, S. H. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10, 495-506.
15. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
16. Valiki, D., & Segireddy, A. R. (2023). Deep Learning Architectures Deployed on Cloud Platforms for Dynamic Financial Risk Evaluation and Market Prediction. American International Journal of Computer Science and Technology, 5(5), 12-24.
17. Pandiri, L., & Singireddy, S. (2023). AI and ML Applications in Dynamic Pricing for Auto and Property Insurance Markets. Journal for ReAttach Therapy and Developmental Diversities, 6, 2206-2223.
18. Malempati, M., Pandiri, L., Paleti, S., & Singireddy, J. (2023). Transforming financial and insurance ecosystems through intelligent automation, secure digital infrastructure, and advanced risk management strategies.
19. Pandiri, L., & Singireddy, S. (2023). AI and ML Applications in Dynamic Pricing for Auto and Property Insurance Markets. Journal for ReAttach Therapy and Developmental Diversities, 6, 2206-2223.
20. Pamisetty, V. (2023). Leveraging artificial intelligence for strategic decision-making in tax administration and policy design. Available at SSRN 5276644.
21. Nandan, B. P. Data Analytics-Driven Approaches to Yield Prediction in Semiconductor Manufacturing. International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering (IJIREEICE), DOI, 10.
22. Kummari, D. N. (2023). Energy Consumption Optimization in Smart Factories Using AI-Based Analytics: Evidence from Automotive Plants. Journal for Reattach Therapy and Development Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3572.
23. Meda, R. (2023). Data Engineering Architectures for Scalable AI in Paint Manufacturing Operations. European data science journal.
24. Koppolu, H. K. R., Sheelam, G. K., & Komaragiri, V. B. (2023). Autonomous Telecommunication Networks: The Convergence of Agentic AI and AI-Optimized Hardware. International Journal of Science and Research (IJSR), 12(12), 2253-2270.
25. Inala, R. (2023). Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 579-606.
26. Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
27. Aitha, A. R. (2023). Cloud-Native Big Data AI/ML Framework for Risk Intelligence and Fraud Control in Banking and Insurance Ecosystems. Available at SSRN 6157967.
28. Nagabhyru, K. C. (2023). Accelerating Digital Transformation with AI Driven Data Engineering: Industry Case Studies from Cloud and IoT Domains. Educational Administration: Theory and Practice, 29(4), 5898-5910.
29. Gottimukkala, V. R. R. (2023). Privacy-Preserving Machine Learning Models for Transaction Monitoring in Global Banking Networks. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 633-652.
30. Raghunath Loganathan (2021). Integrated Risk and Compliance Frameworks for Global Data Center Operations: A Governance-Centric Approach. Universal Journal of Computer Sciences and Communications, 1(1), 1-26. https://doi.org/10.31586/ujscs.2021.1377
31. Kolla, T. (2023). Predictive ETL Failure Detection in Healthcare Data Pipelines Using Anomaly Detection Algorithms. International Journal of Medical Toxicology & Legal Medicine.
32. Ranjith Kumar Peddi (2021). Optimizing Case Management Workflows in Global Data Center Colocation Services. Universal Journal of Computer Sciences and Communications, 1(1), 1-21. https://doi.org/10.31586/ujscs.2021.1380
33. Reddy, V. A. R. (2023). API-First Design As A Strategy For Healthcare System Interoperability. South Eastern European Journal of Public Health, 224–247. Retrieved from https://www.seejph.com/index.php/seejph/article/view/7128
34. Pamisetty, A. (2023). Optimizing National Food Service Supply Chains through Big Data Engineering and Cloud-Native Infrastructure.
35. Bandi, V. D. V. K. AI-Based Anomaly Detection Frameworks in Distributed Enterprise Data Systems.
36. Pamisetty, A. (2023). Integration Of Artificial Intelligence And Machine Learning In National Food Service Distribution Networks. Educational Administration: Theory and Practice, 29 (4), 4979–4994.
37. 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, 10.
38. Recharla, M. (2023). Next-Generation Medicines for Neurological and Neurodegenerative Disorders: From Discovery to Commercialization. Journal of Survey in Fisheries Sciences. https://doi. org/10.53555/sfs. v10i3, 3564.
39. Nandan, B. P. (2021). Enhancing Chip Performance Through Predictive Analytics and Automated Design Verification. Journal of International Crisis and Risk Communication Research, 265-285.
40. Kummari, D. N. (2023). AI-powered demand forecasting for automotive components: A multi-supplier data fusion approach. European Advanced Journal for Emerging Technologies (EAJET)-p-ISSN, 3050-9734.
41. Sheelam, G. K. (2023). Adaptive AI workflows for edge-to-cloud processing in decentralized mobile infrastructure. Journal for Reattach Therapy and Development Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2). 3570ugh Predictive Intelligence.
42. Meda, R. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice.
43. Kolla, S. K. (2023). Explainable AI and ML Models for Transparent Clinical Decision Support. Journal for ReAttach Therapy and Developmental Diversities, 6, 2444-2460.
44. Mangalampalli, B. M. Intelligent Data Profiling for Healthcare Data Lakes Using AI-Enhanced Analytics.
45. Pamisetty, V. (2023). Transforming Community Engagement with Generative AI: Harnessing Machine Learning and Neural Networks for Hunger Alleviation and Global Food Security. Journal for Re Attach Therapy and Developmental Diversities.
46. Mangala, N. (2022). Implementing Databricks Unity Catalog For Centralized Data Governance In Multi-Business-Unitenterprises. Journal of International Crisis and Risk Communication Research, 101-122.
47. Nagubandi, A. R. (2023). 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, 36(6), 653-674.
48. Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
49. Garapati, R. S. (2022). Web-Centric Cloud Framework for Real-Time Monitoring and Risk Prediction in Clinical Trials Using Machine Learning. Current Research in Public Health, 2, 1346.
50. Aitha, A. R. (2023). CloudBased Microservices Architecture for Seamless Insurance Policy Administration. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 607-632.
51. Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN 5697663.
52. Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows.
53. Pamisetty, V. (2023). From Data Silos to Insight: IT Integration Strategies for Intelligent Tax Compliance and Fiscal Efficiency. Available at SSRN 5276875.
54. Segireddy, A. R. (2021). Containerization and Microservices in Payment Systems: A Study of Kubernetes and Docker in Financial Applications. Universal Journal of Business and Management, 1(1), 1-17.
55. Amistapuram, K. (2022). Fraud Detection and Risk Modeling in Insurance: Early Adoption of Machine Learning in Claims Processing. Available at SSRN 5741982.
56. Bandi, V. D. V. K. Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics.
57. Mangalampalli, B. M. (2023). AI-Driven Anomaly Detection in Healthcare Claims Data: A Business Intelligence Perspective. Journal of Rare Cardiovascular Diseases.
58. Kolla, S. K. (2023). Big Data–Driven Machine Learning Frameworks for Clinical Risk Prediction. International Journal of Medical Toxicology and Legal Medicine, 26(3), 44-59.
59. Recharla, M., Nuka, S. T., Chakilam, C., Chava, K., & Suura, S. R. (2023). Next-generation technologies for early disease detection and treatment: harnessing intelligent systems and genetic innovations for improved patient outcomes. Journal for ReAttach Therapy and Developmental Diversities, 6, 1921-1937.
60. Davuluri, P. N. (2019). Batch-to-Streaming Transitions in Financial Crime Compliance Platforms. International Journal Of Engineering And Computer Science, 8(12).
61. Pamisetty, A. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Available at SSRN 5267332.
62. Pandiri, L., & Chitta, S. (2023). AI-Driven Parametric Insurance Models: The Future of Automated Payouts for Natural Disaster and Climate Risk Management. Journal for ReAttach Therapy and Developmental Diversities, 6, 1856-1868.
63. Recharla, M., & Chitta, S. AI-Enhanced Neuroimaging and Deep Learning-Based Early Diagnosis of Multiple Sclerosis and Alzheimer’s.
64. Nandan, B. P., & Chitta, S. S. (2023). Machine Learning Driven Metrology and Defect Detection in Extreme Ultraviolet (EUV) Lithography: A Paradigm Shift in Semiconductor Manufacturing. Educational Administration: Theory and Practice, 29(4), 4555-4568.
65. Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20.
66. Loganathan, R. (2022). Converging Security Architecture and Compliance Management in Enterprise Data Center Ecosystems: A Unified Control Framework. International Journal of Scientific Research and Modern Technology, 1(12), 295–312. https://doi.org/10.38124/ijsrmt.v1i12.1378
67. Meda, R. (2023). Developing AI-Powered Virtual Color Consultation Tools for Retail and Professional Customers. Journal for ReAttach Therapy and Developmental Diversities. https://doi. org/10.53555/jrtdd. v6i10s (2), 3577.
68. Nagabhyru, K. C. (2022). Bridging Traditional ETL Pipelines with AI Enhanced Data Workflows: Foundations of Intelligent Automation in Data Engineering. Available at SSRN 5505199.
69. Inala, R. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493-5505.
70. Aitha, A. R. (2021). Optimizing Data Warehousing for Large Scale Policy Management Using Advanced ETL Frameworks.
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