Intent-Aware Enterprise Architecture Using Persistent AI Agents for Dynamic FinTech Service Composition
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Abstract
As financial technology (FinTech) ecosystems grow increasingly complex, the rigidity of traditional service-focused enterprise architectures (EA) becomes a problem in the face of changing customer intent, diverse data sources and shifting regulatory requirements. This paper gathers insights from twenty-one peer-reviewed publications over the last 14 years (2009 to 2022) and suggests an intent-aware enterprise architecture in which the persistent artificial intelligence (AI) agents continuously interpret the organizational and customer intent to orchestrate dynamic FinTech service composition. From the reviewed literature, 52.4 percent covered the use of artificial intelligence and machine learning in financial services, 38.1 percent covered the coordination of multi-agent system (MAS), and 9.5 percent covered service-oriented enterprise architecture and integration with distributed ledger. The study builds on these three streams, with a five-layer architecture: intent interpretation, persistent agent orchestration, dynamic service composition, domain services, and governance. The synthesis suggests that the explanation of these agents, which are persistent, can be used to increase the flexibility and auditability in coordination compared to traditional service-oriented designs, but also imposes new governance and security requirements. The proposed framework provides the FinTech enterprises with structured guidance on delivering adaptive and intent-driven services and supplies a researchers' framework for empirical testing and validation of the proposed framework.
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References
[1] S. Ahmed, M. M. Alshater, A. El Ammari, and H. Hammami, “Artificial intelligence and machine learning in finance: A bibliometric review,” Res. Int. Bus. Financ., vol. 61, p. 101646, 2022, doi: 10.1016/j.ribaf.2022.101646.
[2] A. Alwadain, E. Fielt, A. Korthaus, and M. Rosemann, “Empirical insights into the development of a service-oriented enterprise architecture,” Data Knowl. Eng., vol. 105, pp. 39–52, 2016, doi: 10.1016/j.datak.2015.09.004.
[3] J. A. P. Antunes, “To supervise or to self-supervise: a machine learning based comparison on credit supervision,” Financ. Innov., vol. 7, Art. no. 1, 2021, doi: 10.1186/s40854-021-00242-4.
[4] B. Anthony Jnr, “Distributed ledger and decentralised technology adoption for smart digital transition in collaborative enterprise,” Enterp. Inf. Syst., vol. 17, no. 4, Art. no. 1989494, Apr. 2023, doi: 10.1080/17517575.2021.1989494.
[5] A. Ashta and H. Herrmann, “Artificial intelligence and fintech: An overview of opportunities and risks for banking, investments, and microfinance,” Strateg. Chang., vol. 30, no. 3, pp. 211–222, May 2021, doi: 10.1002/jsc.2404.
[6] S. Aziz, M. Dowling, H. Hammami, and A. Piepenbrink, “Machine learning in finance: A topic modeling approach,” Eur. Financ. Manag., vol. 28, no. 3, pp. 744–770, Jun. 2022, doi: 10.1111/eufm.12326.
[7] A. Bahrammirzaee, “A comparative survey of artificial intelligence applications in finance: Artificial neural networks, expert system and hybrid intelligent systems,” Neural Comput. Appl., vol. 19, no. 8, pp. 1165–1195, 2010, doi: 10.1007/s00521-010-0362-z.
[8] D. Belanche, L. V Casaló, and C. Flavián, “Artificial intelligence in finTech: understanding robo-advisors adoption among customers,” Ind. Manag. Data Syst., vol. 119, no. 7, pp. 1411–1430, Jun. 2019, doi: 10.1108/IMDS-08-2018-0368.
[9] N. Bussmann, P. Giudici, D. Marinelli, and J. Papenbrock, “Explainable AI in fintech risk management,” Front. Artif. Intell., vol. 3, Art. no. 26, 2020, doi: 10.3389/frai.2020.00026.
[10] R. Calegari, G. Ciatto, V. Mascardi, and A. Omicini, “Logic-based technologies for multi-agent systems: a systematic literature review,” Auton. Agents Multi-Agent Syst., vol. 35, no. 1, pp. 1–67, 2021, doi: 10.1007/s10458-020-09478-3.
[11] L. Cao, “AI in finance: challenges, techniques, and opportunities,” ACM Comput. Surv., vol. 55, no. 3, pp. 1–40, 2022, doi: 10.1145/3502289.
[12] R. C. Cardoso and A. Ferrando, “A review of agent-based programming for multi-agent systems,” Computers, vol. 10, no. 2, Art. no. 16, 2021, doi: 10.3390/computers10020016.
[13] F. Chen and W. Ren, “On the control of multi-agent systems: A survey,” Found. Trends Syst. Control, vol. 6, no. 4, pp. 339–499, 2019, doi: 10.1561/2600000019.
[14] A. Dorri, S. S. Kanhere, and R. Jurdak, “Multi-agent systems: A survey,” IEEE Access, vol. 6, pp. 28573–28593, 2018, doi: 10.1109/ACCESS.2018.2831228.
[15] W. Du and S. Ding, “A survey on multi-agent deep reinforcement learning: from the perspective of challenges and applications,” Artif. Intell. Rev., vol. 54, no. 5, pp. 3215–3238, 2021, doi: 10.1007/s10462-020-09938-y.
[16] J. Huang, J. Chai, and S. Cho, “Deep learning in finance and banking: A literature review and classification,” Front. Bus. Res. China, vol. 14, no. 1, Art. no. 13, 2020, doi: 10.1186/s11782-020-00082-6.
[17] T. T. Nguyen, N. D. Nguyen, and S. Nahavandi, “Deep reinforcement learning for multiagent systems: A review of challenges, solutions, and applications,” IEEE Trans. Cybern., vol. 50, no. 9, pp. 3826–3839, 2020, doi: 10.1109/TCYB.2020.2977374.
[18] E. M. Payne, J. W. Peltier, and V. A. Barger, “Mobile banking and AI-enabled mobile banking: The differential effects of technological and non-technological factors on digital natives’ perceptions and behavior,” J. Res. Interact. Mark., vol. 12, no. 3, pp. 328–346, Oct. 2018, doi: 10.1108/JRIM-07-2018-0087.
[19] M. Wooldridge, An Introduction to MultiAgent Systems, 2nd ed. Hoboken, NJ, USA: John Wiley & Sons, 2009.
[20] D. Zhang, G. Feng, Y. Shi, and D. Srinivasan, “Physical safety and cyber security analysis of multi-agent systems: A survey of recent advances,” IEEE/CAA J. Autom. Sin., vol. 8, no. 2, pp. 319–333, 2021, doi: 10.1109/JAS.2021.1003820.
[21] X. Zheng, M. Zhu, Q. Li, C. Chen, and Y. Tan, “FinBrain: when finance meets AI 2.0,” Front. Inf. Technol. Electron. Eng., vol. 20, no. 7, pp. 914–924, 2019, doi: 10.1631/FITEE.1700822.