Trade to Trust Real-Time AI Compliance for Derivatives
Keywords:
Low Latency Computing, Derivatives Market Infrastructure, Cloud Native Architecture, Microservices Decomposition, Service Mesh Design, Latency Sensitive Workloads, Real Time Trading Systems, Elastic Compute Scaling, Pay Per Use Resource Optimization, Load Balancing Across Availability Zones, Cloud Resilience Engineering, Operational Resilience Risk, Outsourcing Risk Appetite, Intelligent Service Brokerage, High Availability Architectures, Cloud Based Market Systems, Performance Optimized Computing Models, Financial Market Infrastructure Modernization, Scalable Trading Platforms, Resilient Cloud Ecosystems.Abstract
The growing demand for low-latency computing in derivatives markets challenges the traditional approach of monolithic applications implemented in dedicated data center clusters. Cloud-native architectures offer a promising solution due to the flexibility, scalability, and resilience of the cloud. Nevertheless, for latency-sensitive workloads, latency is not only a requirement; cloud-native design principles can lead to improvements in the computing model itself. These include breaking down monolithic applications into microservices deployed in a service mesh, taking advantage of on-demand and pay-per-use resources to optimize costs for off-peak traffic, and improving computing efficiency through loading-balancing across cloud availability zones.
Furthermore, the demonstrably increasing risk of operational resilience in the cloud at a suitably architected cloud level requires organisations and business partners to reconsider their outsourcing risk appetite. Even where a loss occurs, insurance can cover events, as long as the incident doesn't lead to the organisation ceasing to trade for an extended period. For many services, intelligent brokerage can ensure continued service. Examined in more depth, these principles may also strengthen the fundamental efficiency and performance of the models beyond a simple scaling gain.
References
1. Becker, M. (2020). RegTech—the application of modern information technology in regulatory affairs: Areas of interest in research and practice. Intelligent Systems in Accounting, Finance and Management, 27(4), 161–167.
2. Freij, Å. (2020). Using technology to support financial services regulatory compliance: Current applications and future prospects of RegTech. Journal of Investment Compliance, 21(2/3), 181–190.
3. Kolla, S. H. (2022). Strategic Information Integration Models for Cross-Functional Service Optimization in Large-Scale Enterprises. International Journal of Emerging Trends in Engineering and Management Research, 7(3), 11811.
4. Jullum, M., Løland, A., Huseby, R. B., Ånonsen, J., & Lorentzen, J. (2020). Detecting money laundering transactions with machine learning. Journal of Money Laundering Control, 23(1), 173–186.
5. Polak, P., Nelischer, C., Guo, H., & Yang, J. (2020). Intelligent fraud detection and prevention in financial services. Journal of Financial Crime, 27(2), 1–15.
6. von Solms, J. (2020). Integrating regulatory technology (RegTech) into the digital transformation of a bank treasury. Journal of Banking Regulation, 22, 191–207.
7. Arner, D. W., Buckley, R. P., Zetzsche, D. A., & Barberis, J. N. (2020). After the financial crisis: The evolution of FinTech and RegTech. Georgetown Journal of International Law, 50(4), 1271–1319.
8. Carbonneau, A., & Godin, F. (2021). Equal risk pricing of derivatives with deep hedging. Quantitative Finance, 21(4), 593–608.
9. Ivașcu, C.-F. (2021). Option pricing using machine learning. Expert Systems with Applications, 163, 113799.
10. Mangalampalli, B. M. Generative AI Applications In Healthcare Data Mart Design And Optimization.
11. Jang, J. H., Yoon, J., Kim, J., Gu, J., & Kim, H. Y. (2021). DeepOption: A novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods. Information Fusion, 70, 43–59.
12. Tiwari, S., Ramampiaro, H., & Langseth, H. (2021). Machine learning in financial market surveillance: A survey. IEEE Access, 9, 159734–159754.
13. Tsantekidou, E., Passalis, N., Tefas, A., & Kanniainen, J. (2021). Using machine learning to detect anomalous trading activity in financial markets. Expert Systems with Applications, 182, 115211.
14. Hadji Misheva, B., Jaggi, D., Posth, J.-A., Gramespacher, T., & Osterrieder, J. (2021). Audience-dependent explanations for AI-based risk management tools: A survey. Frontiers in Artificial Intelligence, 4, 794996.
15. Amistapuram, K. (2023). Privacy-Preserving Machine Learning Models for Sensitive Customer Data in Insurance Systems. Educational Administration: Theory and Practice, 29(4), 5950-5958.
16. Bussmann, N., Giudici, P., Marinelli, D., & Papenbrock, J. (2021). Explainable machine learning in credit risk management. Computational Economics, 57, 203–216.
17. Jullum, M., Løland, A., & Huseby, R. B. (2021). Predictive monitoring of financial transactions using machine learning. Journal of Money Laundering Control, 24(4), 1–15.
18. Jensen, R. I. T., & Iosifidis, A. (2022). Qualifying and raising anti-money laundering alarms with deep learning. Expert Systems with Applications, 208, 119037.
19. Ratku, A., & Neumann, D. (2022). Derivatives of feed-forward neural networks and their application in real-time market risk management. OR Spectrum, 44, 947–965.
20. Fritz-Morgenthal, S., Hein, B., & Papenbrock, J. (2022). Financial risk management and explainable, trustworthy, responsible AI. Frontiers in Artificial Intelligence, 5, 779799.
21. Chao, X., Ran, Q., Chen, J., Li, T., Qian, Q., & Ergu, D. (2022). Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions. International Review of Financial Analysis, 80, 102023.
22. Grassi, L., & Lanfranchi, D. (2022). RegTech in public and private sectors: The nexus between data, technology and regulation. Journal of Industrial and Business Economics, 49, 441–479.
23. Papantoniou, A. A. (2022). RegTech: Steering the regulatory spaceship in the right direction? Journal of Banking and Financial Technology, 6, 1–16.
24. Currie, W. L., Seddon, J. J. J., & Van Vliet, B. (2022). From decision optimization to satisficing: Regulation of automated trading in the US financial markets. Information & Management, 59(8), 103721.
25. Liu, Q., Wang, C., Zhang, P., & Zheng, K. (2021). Detecting stock market manipulation via machine learning: Evidence from China Securities Regulatory Commission punishment cases. International Review of Financial Analysis, 78, 101887.
26. Zulkifley, M. A., et al. (2023). A survey on stock market manipulation detectors using artificial intelligence. Computers, Materials & Continua, 75(2), 4395–4418.
27. Weber, P., Carl, K. V., & Hinz, O. (2024). Applications of explainable artificial intelligence in finance—a systematic review of finance, information systems, and computer science literature. Management Review Quarterly, 74, 867–907.
28. Pan, M., Li, D., Wu, H., & Lei, P. (2024). Technological revolution and regulatory innovation: How governmental artificial intelligence adoption matters for financial regulation intensity. International Review of Financial Analysis, 96, 103535.
29. Yandamuri, U. S. (2023). An Intelligent Analytics Framework Combining Big Data and Machine Learning for Business Forecasting. Zenodo.
30. Ratzan, J., & Rahman, N. (2023). Measuring responsible artificial intelligence (RAI) in banking: A valid and reliable instrument. AI and Ethics, 3, 1–15.
31. Georgosouli, A. (2023). Metarules, judgment and the algorithmic future of financial regulation in the UK. Oxford Journal of Legal Studies, 43(4), 777–803.
32. Neilson, B. (2023). Artificial intelligence authoring financial recommendations: Comparative Australian evidence. Journal of Financial Regulation, 9(2), 249–257.
33. Achakzai, M. A. K., & Peng, J. (2022). Using machine learning meta-classifiers to detect financial frauds. Finance Research Letters, 48, 102915.
34. Inala, R. Designing Scalable Technology Architectures for Customer Data in Group Insurance and Investment Platforms.
35. Aslam, F., Hunjra, A. I., Ftiti, Z., Louhichi, W., & Shams, T. (2022). Insurance fraud detection: Evidence from artificial intelligence and machine learning. Research in International Business and Finance, 62, 101744.
36. Singh, A., Jain, A., & Biable, S. E. (2022). Financial fraud detection approach based on Firefly optimization algorithm and support vector machine. Applied Computational Intelligence and Soft Computing, 2022, 1468015.
37. Achakzai, M. A. K., & Peng, J. (2023). Detecting financial statement fraud using dynamic ensemble machine learning. International Review of Financial Analysis, 89, 102827.
38. Talukder, M. A., Khalid, M., & Uddin, M. A. (2024). An integrated multistage ensemble machine learning model for fraudulent transaction detection. Journal of Big Data, 11, 168.
39. KollIntegration. South Eastern European Journal of Public Health, 248–260.
40. Hernandez Aros, L., Bustamante Molano, L. X., Gutierrez-Portela, F., Moreno Hernandez, J. J., & Rodríguez Barrero, M. S. (2024). Financial fraud detection through the application of machine learning techniques: A literature review. Humanities and Social Sciences Communications, 11, 1130.
41. Boulieris, P., Pavlopoulos, J., Xenos, A., & Vassalos, V. (2024). Fraud detection with natural language processing. Machine Learning, 113, 5087–5108.
42. Lokanan, M. E. (2024). Predicting money laundering using machine learning and artificial neural networks algorithms in banks. Journal of Applied Security Research, 19(1), 20–44.
43. Jang, J. H., Yoon, J., Kim, J., Gu, J., & Kim, H. Y. (2021). DeepOption: A novel option pricing framework based on deep learning with fused distilled data from multiple parametric methods. Information Fusion, 70, 43–59.
44. a, S. K., & Reddy, V. A. R. (2023). Deep Learning Architectures For Multimodal Medical Data
45. Song, C. (2022). Design and application of financial market option pricing system based on high-performance computing and deep reinforcement learning. Scientific Programming, 2022, 8525361.
46. Dang, Q. V. (2023). Option pricing with machine learning. Ho Chi Minh City University of Education Journal of Science, 20(2), 244–252.
47. Baule, R., & Shkel, D. (2021). Model risk and model choice in the case of barrier options and bonus certificates. Journal of Banking & Finance, 133, 106307.
48. Robisco, A. A., & Carbó Martínez, J. M. (2022). Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction. Financial Innovation, 8, 70.
49. Cosma, S., Rimo, G., & Torluccio, G. (2023). Knowledge mapping of model risk in banking. International Review of Financial Analysis, 89, 102800.
50. Lazar, E., Qi, S., & Tunaru, R. (2024). Measures of model risk for continuous-time finance models. Journal of Financial Econometrics, 22(5), 1456–1481.
51. Weber, P., Carl, K. V., & Hinz, O. (2024). Applications of explainable artificial intelligence in finance—a systematic review of finance, information systems, and computer science literature. Management Review Quarterly, 74, 867–907.
52. Simion, M., & Kelp, C. (2023). Trustworthy artificial intelligence. Asian Journal of Philosophy, 2, 8.
