Regulatory-Grade Evidence for AI in FinTech: A Control-Mapped Framework and Blockchain-Anchored Architecture for Audit Replay

Abstract

Financial institutions increasingly deploy artificial intelligence to support high-stakes operational decisions in anti-money laundering investigations and transaction dispute resolution. However, many deployments fail during audits and regulatory reviews not because models underperform but because decision processes cannot be reliably reconstructed, defended, or proven untampered over time. This paper addresses that gap by proposing a regulatory-grade evidence framework for artificial intelligence (AI) in financial technology (FinTech) that translates governance and documentation expectations into concrete, system-level evidence requirements. Using a document-driven control-mapping methodology, this study synthesizes requirements from the National Institute of Standards and Technology (NIST) Artificial Intelligence Risk Management Framework and the European Union Artificial Intelligence Act into an evidence control taxonomy spanning decision traceability, evidence integrity, audit replay, and retention governance. Two reference architectures are evaluated: a centralized AI governance and logging stack, and the same stack augmented with selective blockchain anchoring to provide tamper-evident integrity guarantees. The analysis identifies evidentiary coverage gaps, architectural trade-offs, and bounded conditions under which blockchain anchoring provides material incremental compliance value. This research contributes an actionable evidence control taxonomy, a controls-to-artifacts traceability matrix, and a Minimum Viable Evidence Layer (MVEL) architecture that enables audit-ready "decision replay" for regulated AI workflows, positioning AI governance in finance as an evidence system design problem.    

Received: 4 February 2026 | Revised: 24 March 2026 | Accepted: 15 April 2026

Conflicts of Interest

The author declares that he has no conflicts of interest to this work.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study

Author Contribution Statement

Ian T. Staley: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Writing – review & editing, Visualization, Project administration.