Secure and Explainable Data Pipelines for Regulatory Compliance: A Cognitive Framework for Financial Services

Abstract

Regulatory reporting pipelines in financial services are often treated as administrative tools, yet they handle highly sensitive data and are subject to strict supervisory requirements. Weaknesses in these pipelines can lead to reporting delays, inconsistent audit trails, and security incidents. Recent regulatory guidance, including BCBS 239 and the EU’s Digital Operational Resilience Act (DORA), highlights the need for reliable and explainable compliance infrastructures. However, technical mechanisms for ensuring tamper-resilient logging and transparent model behavior remain limited in industry practice. This paper introduces a secure and explainable data pipeline framework for regulatory compliance. The approach integrates four elements: secure data ingestion with cryptographic lineage, interpretable analytics, tamper-evident governance logs, and managerial reporting tailored to supervisory dialogue. A case study from one multinational bank illustrates how the framework was applied in practice. The implementation suggested measurable improvements in audit trail completeness and reporting efficiency, with modest storage and performance overheads. Results should be interpreted cautiously: they reflect a single institution, under specific operational conditions, and without broad statistical validation. The contribution of this work lies in adapting established security and governance mechanisms—such as Merkle tree-based logging, explainable machine learning methods, and privileged access controls—to the context of regulatory compliance. The study highlights both the potential benefits and the limitations of embedding security-by-design in compliance data pipelines.

Keywords: Explainable AI, Financial services, Regulatory compliance, Tamper-evident logging, Secure data pipelines.

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