DESIGNING AI-NATIVE FINANCIAL SYSTEMS: ARCHITECTURE PATTERNS FOR INTELLIGENT ENTERPRISE PLATFORMS

AJAY MIRANI

doi:10.5281/zenodo.20215472

Authors

AJAY MIRANI Independent Researcher, USA

DOI:

https://doi.org/10.5281/zenodo.20215472

Keywords:

adaptive schema; AI-native architecture; architecture patterns; compliance-by-design; enterprise financial platforms; event-driven intelligence; explainability-first; federated AI governance

Abstract

Enterprise financial platforms have historically been designed as systems of record augmented post-hoc with analytical and AI capabilities — an approach that imposes a compounding integration tax that grows superlinearly with platform complexity. This paper argues that AI must be treated as a foundational architectural concern from platform inception, not a layered addition, and introduces five architecture patterns for AI-native financial system design: Event-Driven Intelligence, Federated AI Governance, Compliance-by-Design, Adaptive Schema Fabric, and Explainability-First. These patterns, derived from practitioner experience designing and operating enterprise-scale financial platforms at a high-volume global technology manufacturer, collectively define a reference architecture for AI-native financial platforms. The paper further presents a simulated case study of an AI-native order-to-cash replatforming initiative and a comparative evaluation against traditional layered and bolt-on AI architectures. The Compliance-by-Design pattern explicitly integrates the Adaptive Compliance Intelligence and Governance (ACIG) framework as its implementation blueprint, establishing a formal connection between platform-level AI-native design and domain-specific compliance intelligence. Together, the two papers constitute a cohesive architectural framework for intelligent enterprise financial governance.

Author Biography

AJAY MIRANI, Independent Researcher, USA

Independent Researcher, USA

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