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All Journal The Eastasouth Management and Business The Eastasouth Journal of Information System and Computer Science
Nithesh Gudipuri
Raymond James & Associates, Independent Researcher, St. Petersburg, FL 33716, USA
Computer Science & IT Decision Sciences, Operations Research & Management

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Causality-Preserving Distributed Decision Fabrics for AI-Native Financial Enterprise Architectures Nithesh Gudipuri
The Eastasouth Management and Business Vol. 1 No. 03 (2023): The Eastasouth Management and Business (ESMB)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esmb.v1i03.1169

Abstract

Today, financial enterprise architectures are increasingly incorporating artificial intelligence (AI) capabilities into distributed transaction-processing systems, creating a tension between the need for rapid service availability and the need for a causally coherent audit trail of AI-made decisions. In this paper, we marshal research on causal consistency models, causal inference, explainable AI (XAI), and blockchain-based financial infrastructure to propose a conceptual framework called the causality-preserving distributed decision fabric. Causal consistency, which is formalized in causal-memory and session-guarantee models, is explored as a compromise between the high availability of eventual consistency and the coordination cost of linearizability, which is limited by the CAP theorem. Methods for causal inference and model-agnostic explainability, such as Shapley-value attribution and local surrogate modeling, are examined as ways to make automated financial decisions interpretable to those who manage the risks in financial institutions and regulators. Distributed ledger architectures are viewed as an audit trail that can track and order AI-backed decisions between organizational lines. The synthesis suggests that none of the single mechanisms alone meet the simultaneous requirements of availability, explainability, and auditability; thus, a layered architecture of causal-consistency protocols, an explainability layer, and a permissioned ledger is proposed. Comparisons on a conceptual level regarding consistency models and explainability methods, as well as architectural diagrams depicting the proposed layering, are presented. The findings suggest that maintaining causality is a key prerequisite for trustworthy AI-native financial systems, but that interoperability and computational load are major obstacles to implementation.
Intent-Aware Enterprise Architecture Using Persistent AI Agents for Dynamic FinTech Service Composition Nithesh Gudipuri
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 01 (2024): The Eastasouth Journal of Information System and Computer Science (ESISCS)
Publisher : Eastasouth Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58812/esiscs.v2i01.1170

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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