A FinTech-Oriented Data Mesh and Lakehouse Architecture for Governed, Real-Time Financial Analytics

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.