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All Journal The Eastasouth Journal of Information System and Computer Science
Gopinathan Rathinavelu
Data Architect, Arizona, USA
Computer Science & IT

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AI Governance: Automatically Checking AI Systems Against the EU AI Act and NIST Rules Gopinathan Rathinavelu
The Eastasouth Journal of Information System and Computer Science Vol. 2 No. 03 (2025): 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.v2i03.1257

Abstract

Financial institutions deploying models for credit evaluation, fraud detection, and anti-money laundering must demonstrate that their internal documentation satisfies frameworks such as the European Union's Artificial Intelligence Act (EU AI Act) and the NIST AI Risk Management Framework (AI RMF). Automating this documentation review is attractive, but a checker that approves a control merely because a document mentions the right topic keywords is dangerous: it converts empty corporate assurance into apparent compliance. We present a system design and evaluation for evidence-linked compliance checking, with three contributions. First, a structured control catalog of twelve documentation-checkable controls mapped to EU AI Act articles and NIST AI RMF functions, each with explicit substance criteria. Second, an evidence-linked checker architecture whose every "satisfied" decision is tied to an exact supporting sentence, supported by a tiered decision pipeline, calibrated abstention, and a reviewer workflow. Third, a controlled synthetic benchmark of financial AI documentation in which each control is substantive, boilerplate-only, or absent, with disjoint training and unseen phrasing sets. In our baseline experiments, keyword rules falsely approve 85% of boilerplate-only controls. Trained sparse lexical classifiers reach high precision but, under unseen phrasing, recall falls to between 0.12 and 0.22. Adding 25 labeled documents in the new phrasing restores recall above 0.99, an estimate we caution is optimistic given the synthetic setting. Removal-based deletion tests show that evidence-faithfulness metrics must be interpreted against the redundancy of evidence within a document. The benchmark is a measurement tool for checker reliability, not a legal determination of conformity.
A FinTech-Oriented Data Mesh and Lakehouse Architecture for Governed, Real-Time Financial Analytics Gopinathan Rathinavelu
The Eastasouth Journal of Information System and Computer Science Vol. 1 No. 01 (2023): 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.v1i01.1308

Abstract

Financial institutions depend on timely, trustworthy, and traceable data to support fraud detection, anti-money-laundering (AML) monitoring, credit-risk assessment, payment operations, customer analytics, and regulatory reporting. Centralized data platforms can become delivery bottlenecks when business domains use different systems, definitions, access models, and release cycles. This paper proposes a FinTech-oriented enterprise data platform that combines Data Mesh principles with a Lakehouse foundation, and describes it through three complementary views: a platform overview that separates data creators from data consumers, a logical architecture in which domain-owned storage and compute are exposed as one logical lakehouse under governance, operational, and development planes, and an operating model in which domain, platform, and governance teams publish bronze, silver, and gold data layers. Domains such as payments, cards, customer identity, lending, fraud, and compliance publish governed data products through standard contracts. A shared platform provides batch and streaming ingestion, open table storage, virtualization and federated querying, metadata discovery, policy-based access, lineage, quality checks, and automated deployment. Federated computational governance sets common controls while domain teams manage products close to their business context. The paper presents requirements, components, representative financial workflows, governance controls, and an evaluation plan. It reports a reference design, not a measured deployment; expected benefits are therefore stated as hypotheses to be tested in a pilot.
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