Articles
AI-Powered Big Data Analytics for Early Detection of Financial Market Anomalies
Abstract
The past decade has witnessed a proliferation of Big Data in the Finance industry. Several technologies are being developed in parallel, and a consolidated assortment of AI-based analytical products is emerging. The bulk of these Big Data-based projects within the finance domain are concerned with Investment Management. These services deal with the application of Machine Learning or Natural Language Processing on alternative datasets (e.g. Unstructured Data, Sentiment Analysis, Web traffic, etc.) with the aim of predicting Price Movements or even Stock Prices of Stocks, commodities, or different Asset Classes. Another set of companies are providing data-driven strategies that predict Financial Market Anomalies. But these innovative products do not appear to provide reliable and robust results for Early Detection of Financial Anomalies.
One technology that aims to contribute in this area is AI-Powered Anomaly Detection. Early detection of Financial anomalies (e.g. Flash Crashes, High Liquidity Drought, Opening Price Dislocation, Price Dislocation in the presence of Large Imbalance) has been attempted through traditional machine learning-based models. A natural extension of this effort is to employ AI-Powered Big Data Analytics for Early Detection of Financial AAnomalies. An end-to-end solution capable of ingesting Big Data in real time, processing with AI-Powered Technology, and signalling detections with Data Governance is developed and presented. Real-time Scalability and versioning of the data has also been examined.
Article Information
Journal |
International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
|---|---|
Volume (Issue) |
Vol. 5 No. 6 (2022): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) |
DOI |
|
Pages |
10427-10441 |
Published |
December 6, 2022 |
| Copyright | |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Vinod Battapothu (2022). AI-Powered Big Data Analytics for Early Detection of Financial Market Anomalies. International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , Vol. 5 No. 6 (2022): International Journal of Advanced Engineering Science and Information Technology (IJAESIT) , pp. 10427-10441. https://doi.org/10.15662/IJAESIT.2022.0506004 |
References
1. Ahmed, S., et al. (2022). The impact of real-time big data processing on financial risk assessment in the UAE. Journal of Financial Risk Management, 11(2), 145–162.
2. 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.
3. Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31.
4. Uday Surendra Yandamuri. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706. https://doi.org/10.5281/zenodo.18095256
5. AlJaloudi, O., Thiam, M., Abdel Qader, M., Al-Mhdawi, M. K. S., Qazi, A., & Dacre, N.
6. Abdullah, A., Omolola, H., Taiwo, S., & Aderibigbe, O. Advanced AI Solutions for Securities Trading: Building Scalable and Optimized Systems for Global Financial Markets. International Journal on Cybernetics & Informatics, 13(3), 31–45.
7. Bates, D. W., Saria, S., Ohno-Machado, L., et al. (2014). Big data in health care. Health Affairs, 33(7), 1123–1131.
8. Keerthi Amistapuram. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950–5958. https://doi.org/10.53555/kuey.v29i4.10965
9. Al-Mhdawi, M. K. S., Qazi, A., & Dacre, N. (2023). Generative AI and the "black-box" nature of risk management: A systematic review. Journal of Business Research, 158, 113–128.
10. Bansal, R. Machine learning algorithms for automated trading and data-driven decision-making. Journal of Investment Strategies, 13(1), 45–60.
11. Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. ACM SIGMOD Record, 29(2), 93–104.
12. Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment. (2023). American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140) , 1(1). https://aojse.com/index.php/aojse/article/view/19
13. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.
14. Siva Hemanth Kolla. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2489–2502. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4774
15. Cios, K. J., & Moore, G. W. (2002). Uniqueness of medical data mining. Artificial Intelligence in Medicine, 26(1–2), 1–24.
16. Kummari, D. N., & Burugulla, J. K. R. (2023). Decision Support Systems for Government Auditing: The Role of AI in Ensuring Transparency and Compliance. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 493-532.
17. Danielsson, J., Macrae, R., & Uthemann, A. (2022). Artificial intelligence and systemic risk. Journal of Banking & Finance, 140, 106–125.
18. Varri, D. B. S. (2023). Advanced Threat Intelligence Modeling for Proactive Cyber Defense Systems. Available at SSRN 5774926.
19. Dwork, C. (2008). Differential privacy. ICALP Proceedings, 1–12.
20. Bandi, V. D. V. K. (2023). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205. Retrieved from https://www.seejph.com/index.php/seejph/article/view/7057
21. Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1–19. Retrieved from https://www.scipublications.com/journal/index.php/crph/article/view/1372
22. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
23. Maguluri, K. K., Pandugula, C., Kalisetty, S., & Mallesham, G. (2022). Advancing Pain Medicine with AI and Neural Networks: Predictive Analytics and Personalized Treatment Plans for Chronic and Acute Pain Managements. Journal of Artificial Intelligence and Big Data, 2(1), 112-126.
24. Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
25. Goldstein, M., & Uchida, S. (2016). A comparative evaluation of unsupervised anomaly detection algorithms. Pattern Recognition, 64, 206–223.
26. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
27. 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. Retrieved from https://www.scipublications.com/journal/index.php/ujbm/article/view/1352
28. He, J., Baxter, S. L., Xu, J., et al. (2019). The practical implementation of AI in healthcare. Nature Medicine, 25(1), 30–36.
29. Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
30. Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping. JAMIA, 20(1), 117–121.
31. Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows.
32. Iglewicz, B., & Hoaglin, D. C. (1993). How to detect and handle outliers. ASQC.
33. Johnson, A. E. W., Pollard, T. J., Shen, L., et al. (2016). MIMIC-III database. Scientific Data, 3, 160035.
34. Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology, 1(12), 227–237. https://doi.org/10.38124/ijsrmt.v1i12.1111
35. Kimball, R., & Caserta, J. (2004). The data warehouse ETL toolkit. Wiley.
36. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
37. Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2009). Outlier detection in axis-parallel subspaces. PKDD Proceedings, 831–838.
38. 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 en e-ISSN 3050-9742, 1(1).
39. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
40. 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.
41. Varri, D. B. S. (2022). A Framework for Cloud-Integrated Database Hardening in Hybrid AWS-Azure Environments: Security Posture Automation Through Wiz-Driven Insights. International Journal of Scientific Research and Modern Technology, 1(12), 216-226.
42. Malhotra, P., Vig, L., Shroff, G., & Agarwal, P. (2015). Long short-term memory networks for anomaly detection. ESANN Proceedings.
43. Kalisetty, S., Vankayalapati, R. K., Reddy, L., Sondinti, K., & Valiki, S. (2022). AI-Native Cloud Platforms: Redefining Scalability and Flexibility in Artificial Intelligence Workflows. Linguistic and Philosophical Investigations, 21(1), 1-15.
44. Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
45. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare. Briefings in Bioinformatics, 19(6), 1236–1246.
46. Kushvanth Chowdary Nagabhyru. (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. https://doi.org/10.53555/kuey.v29i4.10932
47. Murphy, S. N., Weber, G., Mendis, M., et al. (2010). i2b2 platform. JAMIA, 17(2), 124–130.
48. Goutham Kumar Sheelam, Hara Krishna Reddy Koppolu. (2022). Data Engineering And Analytics For 5G-Driven Customer Experience In Telecom, Media, And Healthcare. Migration Letters, 19(S2), 1920–1944. Retrieved from https://migrationletters.com/index.php/ml/article/view/11938.
49. Patcha, A., & Park, J. M. (2007). An overview of anomaly detection techniques. Computer Networks, 51(12), 3448–3470.
50. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn. Journal of Machine Learning Research, 12, 2825–2830.
51. 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.
52. Rajkomar, A., Oren, E., Chen, K., et al. (2018). Scalable deep learning with EHRs. NPJ Digital Medicine, 1, 18.
53. Avinash Reddy Segireddy. (2022). Terraform and Ansible in Building Resilient Cloud-Native Payment Architectures. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 444–455. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7905.
54. Ringberg, H., Soule, A., Rexford, J., & Diot, C. (2007). Sensitivity of PCA for anomaly detection. SIGMETRICS Proceedings.
55. 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.
56. Ruff, L., Vandermeulen, R. A., Görnitz, N., et al. (2018). Deep one-class classification. ICML Proceedings.
57. Rongali, S. K. (2023). Explainable Artificial Intelligence (XAI) Framework for Transparent Clinical Decision Support Systems. International Journal of Medical Toxicology and Legal Medicine, 26(3), 22-31.
58. Salfner, F., Lenk, M., & Malek, M. (2010). Survey of failure prediction methods. ACM Computing Surveys, 42(3), 1–42.
59. 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.
60. Schölkopf, B., Platt, J. C., Shawe-Taylor, J., et al. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
61. Kalisetty, S., & Ganti, V. K. A. T. (2019). Transforming the Retail Landscape: Srinivas’s Vision for Integrating Advanced Technologies in Supply Chain Efficiency and Customer Experience. Online Journal of Materials Science, 1, 1254.
62. Sipos, R., Fradkin, D., Moerchen, F., & Wang, Z. (2014). Log-based predictive maintenance. KDD Proceedings.
63. Meda, R. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice.
64. Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/wjcmr/article/view/1376
65. Bandi, V. D. V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 2(12), 78–90. https://doi.org/10.38124/ijsrmt.v2i12.1236
66. Inala, R. Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective.
67. Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society B, 58(1), 267–288.
68. 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.
69. Amistapuram, K. (2022). Fraud Detection and Risk Modeling in Insurance: Early Adoption of Machine Learning in Claims Processing. Available at SSRN 5741982.
70. AI Powered Fraud Detection Systems: Enhancing Risk Assessment in the Insurance Sector. (2023). American Journal of Analytics and Artificial Intelligence (ajaai) With ISSN 3067-283X, 1(1). https://ajaai.com/index.php/ajaai/article/view/14
71. Weber, G. M., Mandl, K. D., & Kohane, I. S. (2014). Finding the missing link for big biomedical data. JAMIA, 21(1), 1–3.
72. Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/ojes/article/view/1360
73. Guntupalli, R. (2023). AI-Driven Threat Detection and Mitigation in Cloud Infrastructure: Enhancing Security through Machine Learning and Anomaly Detection. Available at SSRN 5329158.
74. Zhang, Y., & Yang, Q. (2021). A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering, 34(12), 5586–5609.
75. 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.
76. Almadhoun, R., Kadadha, M., Al-Fuqaha, A., & Guizani, M. (2021). A user-centric blockchain-based system for incident response in the era of IoT. Internet of Things, 14, 100371. https://doi.org/10.1016/j.iot.2021.100371
77. Kalisetty, S. (2023). The Role of Circular Supply Chains in Achieving Sustainability Goals: A 2023 Perspective on Recycling, Reuse, and Resource Optimization. Reuse, and Resource Optimization (June 15, 2023).
78. Brown, T., & Lee, J. (2022). Statistical methods for detecting misstatements in financial audits: A regression analysis approach. Audit & Finance Review, 29(4), 210–225..
79. Siva Hemanth Kolla. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 495–506. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8037
80. Bishop, C. M. (1994). Novelty detection and neural network validation. IEE Proceedings, 141(4), 217–222.
81. Rongali, S. K. (2022). AI-Driven Automation in Healthcare Claims and EHR Processing Using MuleSoft and Machine Learning Pipelines. Available at SSRN 5763022.
82. Cook, D. J., & Holder, L. B. (2006). Mining graph data. Wiley.
83. Kelly, B., & Xiu, D. (2023). Financial machine learning: A review and prospects. Journal of Financial Economics, 145(2), 310–335.
84. Bluwstein, K., et al. (2023). Credit cycles and the prediction of financial crises using machine learning. Journal of Banking & Finance, 140, 106–122.
85. Kumar, A., Gupta, P., & Singh, R. (2023). Sentiment analysis methods for proactive brand reputation risk management. International Journal of Information Management Data Insights, 3(1).
86. Ramesh Inala. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493–5505. https://doi.org/10.53555/kuey.v29i4.10424
87. Aggarwal, C. C. (2017). Outlier analysis (2nd ed.). Springer.
88. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
89. Bifet, A., & Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. SDM Proceedings.
90. Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN 5697663.
91. Su, T., et al. Anomaly Detection and Risk Early Warning System for Financial Time Series Based on the WaveLST-Trans Model. Technological Forecasting and Social Change, 188, 122–139.
92. Avinash Reddy Aitha. (2022). Deep Neural Networks for Property Risk Prediction Leveraging Aerial and Satellite Imaging. International Journal of Communication Networks and Information Security (IJCNIS), 14(3), 1308–1318. Retrieved from https://www.ijcnis.org/index.php/ijcnis/article/view/8609
93. Guntupalli, R. (2023). Optimizing Cloud Infrastructure Performance Using AI: Intelligent Resource Allocation and Predictive Maintenance. Available at SSRN 5329154
2. 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.
3. Ahmed, M., Mahmood, A. N., & Hu, J. (2016). A survey of network anomaly detection techniques. Journal of Network and Computer Applications, 60, 19–31.
4. Uday Surendra Yandamuri. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. International Journal Of Finance, 36(6), 682-706. https://doi.org/10.5281/zenodo.18095256
5. AlJaloudi, O., Thiam, M., Abdel Qader, M., Al-Mhdawi, M. K. S., Qazi, A., & Dacre, N.
6. Abdullah, A., Omolola, H., Taiwo, S., & Aderibigbe, O. Advanced AI Solutions for Securities Trading: Building Scalable and Optimized Systems for Global Financial Markets. International Journal on Cybernetics & Informatics, 13(3), 31–45.
7. Bates, D. W., Saria, S., Ohno-Machado, L., et al. (2014). Big data in health care. Health Affairs, 33(7), 1123–1131.
8. Keerthi Amistapuram. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950–5958. https://doi.org/10.53555/kuey.v29i4.10965
9. Al-Mhdawi, M. K. S., Qazi, A., & Dacre, N. (2023). Generative AI and the "black-box" nature of risk management: A systematic review. Journal of Business Research, 158, 113–128.
10. Bansal, R. Machine learning algorithms for automated trading and data-driven decision-making. Journal of Investment Strategies, 13(1), 45–60.
11. Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). LOF: Identifying density-based local outliers. ACM SIGMOD Record, 29(2), 93–104.
12. Unifying Data Engineering and Machine Learning Pipelines: An Enterprise Roadmap to Automated Model Deployment. (2023). American Online Journal of Science and Engineering (AOJSE) (ISSN: 3067-1140) , 1(1). https://aojse.com/index.php/aojse/article/view/19
13. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19(2), 171–209.
14. Siva Hemanth Kolla. (2023). Deep Learning–Driven Retrieval-Augmented Generation for Enterprise ITSM Automation: A Governance-Aligned Large Language Model Architecture. Journal of Computational Analysis and Applications (JoCAAA), 31(4), 2489–2502. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/4774
15. Cios, K. J., & Moore, G. W. (2002). Uniqueness of medical data mining. Artificial Intelligence in Medicine, 26(1–2), 1–24.
16. Kummari, D. N., & Burugulla, J. K. R. (2023). Decision Support Systems for Government Auditing: The Role of AI in Ensuring Transparency and Compliance. International Journal of Finance (IJFIN)-ABDC Journal Quality List, 36(6), 493-532.
17. Danielsson, J., Macrae, R., & Uthemann, A. (2022). Artificial intelligence and systemic risk. Journal of Banking & Finance, 140, 106–125.
18. Varri, D. B. S. (2023). Advanced Threat Intelligence Modeling for Proactive Cyber Defense Systems. Available at SSRN 5774926.
19. Dwork, C. (2008). Differential privacy. ICALP Proceedings, 1–12.
20. Bandi, V. D. V. K. (2023). Production-Grade Machine Learning Pipelines For Healthcare Predictive Analytics. South Eastern European Journal of Public Health, 189–205. Retrieved from https://www.seejph.com/index.php/seejph/article/view/7057
21. Kolla, S. K. (2021). Architectural Frameworks for Large-Scale Electronic Health Record Data Platforms. Current Research in Public Health, 1(1), 1–19. Retrieved from https://www.scipublications.com/journal/index.php/crph/article/view/1372
22. Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.
23. Maguluri, K. K., Pandugula, C., Kalisetty, S., & Mallesham, G. (2022). Advancing Pain Medicine with AI and Neural Networks: Predictive Analytics and Personalized Treatment Plans for Chronic and Acute Pain Managements. Journal of Artificial Intelligence and Big Data, 2(1), 112-126.
24. Garapati, R. S. (2022). AI-Augmented Virtual Health Assistant: A Web-Based Solution for Personalized Medication Management and Patient Engagement. Available at SSRN 5639650.
25. Goldstein, M., & Uchida, S. (2016). A comparative evaluation of unsupervised anomaly detection algorithms. Pattern Recognition, 64, 206–223.
26. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
27. 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. Retrieved from https://www.scipublications.com/journal/index.php/ujbm/article/view/1352
28. He, J., Baxter, S. L., Xu, J., et al. (2019). The practical implementation of AI in healthcare. Nature Medicine, 25(1), 30–36.
29. Inala, R. AI-Powered Investment Decision Support Systems: Building Smart Data Products with Embedded Governance Controls.
30. Hripcsak, G., & Albers, D. J. (2013). Next-generation phenotyping. JAMIA, 20(1), 117–121.
31. Gottimukkala, V. R. R. (2021). Digital Signal Processing Challenges in Financial Messaging Systems: Case Studies in High-Volume SWIFT Flows.
32. Iglewicz, B., & Hoaglin, D. C. (1993). How to detect and handle outliers. ASQC.
33. Johnson, A. E. W., Pollard, T. J., Shen, L., et al. (2016). MIMIC-III database. Scientific Data, 3, 160035.
34. Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology, 1(12), 227–237. https://doi.org/10.38124/ijsrmt.v1i12.1111
35. Kimball, R., & Caserta, J. (2004). The data warehouse ETL toolkit. Wiley.
36. Davuluri, P. N. Integrating Artificial Intelligence into Event-Driven Financial Crime Compliance Platforms.
37. Kriegel, H. P., Kröger, P., Schubert, E., & Zimek, A. (2009). Outlier detection in axis-parallel subspaces. PKDD Proceedings, 831–838.
38. 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 en e-ISSN 3050-9742, 1(1).
39. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
40. 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.
41. Varri, D. B. S. (2022). A Framework for Cloud-Integrated Database Hardening in Hybrid AWS-Azure Environments: Security Posture Automation Through Wiz-Driven Insights. International Journal of Scientific Research and Modern Technology, 1(12), 216-226.
42. Malhotra, P., Vig, L., Shroff, G., & Agarwal, P. (2015). Long short-term memory networks for anomaly detection. ESANN Proceedings.
43. Kalisetty, S., Vankayalapati, R. K., Reddy, L., Sondinti, K., & Valiki, S. (2022). AI-Native Cloud Platforms: Redefining Scalability and Flexibility in Artificial Intelligence Workflows. Linguistic and Philosophical Investigations, 21(1), 1-15.
44. Garapati, R. S. (2023). Optimizing Energy Consumption in Smart Build-ings Through Web-Integrated AI and Cloud-Driven Control Systems.
45. Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare. Briefings in Bioinformatics, 19(6), 1236–1246.
46. Kushvanth Chowdary Nagabhyru. (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. https://doi.org/10.53555/kuey.v29i4.10932
47. Murphy, S. N., Weber, G., Mendis, M., et al. (2010). i2b2 platform. JAMIA, 17(2), 124–130.
48. Goutham Kumar Sheelam, Hara Krishna Reddy Koppolu. (2022). Data Engineering And Analytics For 5G-Driven Customer Experience In Telecom, Media, And Healthcare. Migration Letters, 19(S2), 1920–1944. Retrieved from https://migrationletters.com/index.php/ml/article/view/11938.
49. Patcha, A., & Park, J. M. (2007). An overview of anomaly detection techniques. Computer Networks, 51(12), 3448–3470.
50. Pedregosa, F., Varoquaux, G., Gramfort, A., et al. (2011). Scikit-learn. Journal of Machine Learning Research, 12, 2825–2830.
51. 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.
52. Rajkomar, A., Oren, E., Chen, K., et al. (2018). Scalable deep learning with EHRs. NPJ Digital Medicine, 1, 18.
53. Avinash Reddy Segireddy. (2022). Terraform and Ansible in Building Resilient Cloud-Native Payment Architectures. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 444–455. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/7905.
54. Ringberg, H., Soule, A., Rexford, J., & Diot, C. (2007). Sensitivity of PCA for anomaly detection. SIGMETRICS Proceedings.
55. 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.
56. Ruff, L., Vandermeulen, R. A., Görnitz, N., et al. (2018). Deep one-class classification. ICML Proceedings.
57. Rongali, S. K. (2023). Explainable Artificial Intelligence (XAI) Framework for Transparent Clinical Decision Support Systems. International Journal of Medical Toxicology and Legal Medicine, 26(3), 22-31.
58. Salfner, F., Lenk, M., & Malek, M. (2010). Survey of failure prediction methods. ACM Computing Surveys, 42(3), 1–42.
59. 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.
60. Schölkopf, B., Platt, J. C., Shawe-Taylor, J., et al. (2001). Estimating the support of a high-dimensional distribution. Neural Computation, 13(7), 1443–1471.
61. Kalisetty, S., & Ganti, V. K. A. T. (2019). Transforming the Retail Landscape: Srinivas’s Vision for Integrating Advanced Technologies in Supply Chain Efficiency and Customer Experience. Online Journal of Materials Science, 1, 1254.
62. Sipos, R., Fradkin, D., Moerchen, F., & Wang, Z. (2014). Log-based predictive maintenance. KDD Proceedings.
63. Meda, R. (2023). Intelligent Infrastructure for Real-Time Inventory and Logistics in Retail Supply Chains. Educational Administration: Theory and Practice.
64. Kolla, S. K. (2021). Designing Scalable Healthcare Data Pipelines for Multi-Hospital Networks. World Journal of Clinical Medicine Research, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/wjcmr/article/view/1376
65. Bandi, V. D. V. K. (2023). Cloud-Native Model Lifecycle Management for Enterprise AI Systems. International Journal of Scientific Research and Modern Technology, 2(12), 78–90. https://doi.org/10.38124/ijsrmt.v2i12.1236
66. Inala, R. Revolutionizing Customer Master Data in Insurance Technology Platforms: An AI and MDM Architecture Perspective.
67. Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society B, 58(1), 267–288.
68. 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.
69. Amistapuram, K. (2022). Fraud Detection and Risk Modeling in Insurance: Early Adoption of Machine Learning in Claims Processing. Available at SSRN 5741982.
70. AI Powered Fraud Detection Systems: Enhancing Risk Assessment in the Insurance Sector. (2023). American Journal of Analytics and Artificial Intelligence (ajaai) With ISSN 3067-283X, 1(1). https://ajaai.com/index.php/ajaai/article/view/14
71. Weber, G. M., Mandl, K. D., & Kohane, I. S. (2014). Finding the missing link for big biomedical data. JAMIA, 21(1), 1–3.
72. Kolla, S. H. (2021). Rule-Based Automation for IT Service Management Workflows. Online Journal of Engineering Sciences, 1(1), 1–14. Retrieved from https://www.scipublications.com/journal/index.php/ojes/article/view/1360
73. Guntupalli, R. (2023). AI-Driven Threat Detection and Mitigation in Cloud Infrastructure: Enhancing Security through Machine Learning and Anomaly Detection. Available at SSRN 5329158.
74. Zhang, Y., & Yang, Q. (2021). A survey on multi-task learning. IEEE Transactions on Knowledge and Data Engineering, 34(12), 5586–5609.
75. 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.
76. Almadhoun, R., Kadadha, M., Al-Fuqaha, A., & Guizani, M. (2021). A user-centric blockchain-based system for incident response in the era of IoT. Internet of Things, 14, 100371. https://doi.org/10.1016/j.iot.2021.100371
77. Kalisetty, S. (2023). The Role of Circular Supply Chains in Achieving Sustainability Goals: A 2023 Perspective on Recycling, Reuse, and Resource Optimization. Reuse, and Resource Optimization (June 15, 2023).
78. Brown, T., & Lee, J. (2022). Statistical methods for detecting misstatements in financial audits: A regression analysis approach. Audit & Finance Review, 29(4), 210–225..
79. Siva Hemanth Kolla. (2022). Knowledge Retrieval Systems for Enterprise Service Environments. International Journal of Intelligent Systems and Applications in Engineering, 10(3s), 495–506. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8037
80. Bishop, C. M. (1994). Novelty detection and neural network validation. IEE Proceedings, 141(4), 217–222.
81. Rongali, S. K. (2022). AI-Driven Automation in Healthcare Claims and EHR Processing Using MuleSoft and Machine Learning Pipelines. Available at SSRN 5763022.
82. Cook, D. J., & Holder, L. B. (2006). Mining graph data. Wiley.
83. Kelly, B., & Xiu, D. (2023). Financial machine learning: A review and prospects. Journal of Financial Economics, 145(2), 310–335.
84. Bluwstein, K., et al. (2023). Credit cycles and the prediction of financial crises using machine learning. Journal of Banking & Finance, 140, 106–122.
85. Kumar, A., Gupta, P., & Singh, R. (2023). Sentiment analysis methods for proactive brand reputation risk management. International Journal of Information Management Data Insights, 3(1).
86. Ramesh Inala. (2023). Big Data Architectures for Modernizing Customer Master Systems in Group Insurance and Retirement Planning. Educational Administration: Theory and Practice, 29(4), 5493–5505. https://doi.org/10.53555/kuey.v29i4.10424
87. Aggarwal, C. C. (2017). Outlier analysis (2nd ed.). Springer.
88. Davuluri, P. N. AI-Augmented Sanctions Screening: Enhancing Accuracy and Latency in Real Time Compliance Systems.
89. Bifet, A., & Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. SDM Proceedings.
90. Nagabhyru, K. C. (2023). From Data Silos to Knowledge Graphs: Architecting CrossEnterprise AI Solutions for Scalability and Trust. Available at SSRN 5697663.
91. Su, T., et al. Anomaly Detection and Risk Early Warning System for Financial Time Series Based on the WaveLST-Trans Model. Technological Forecasting and Social Change, 188, 122–139.
92. Avinash Reddy Aitha. (2022). Deep Neural Networks for Property Risk Prediction Leveraging Aerial and Satellite Imaging. International Journal of Communication Networks and Information Security (IJCNIS), 14(3), 1308–1318. Retrieved from https://www.ijcnis.org/index.php/ijcnis/article/view/8609
93. Guntupalli, R. (2023). Optimizing Cloud Infrastructure Performance Using AI: Intelligent Resource Allocation and Predictive Maintenance. Available at SSRN 5329154