Embedding Legal Norms into AI Workflows: A Framework for Algorithmic Compliance in Finance
Keywords:
Algorithmic compliance, explainable artificial intelligence, financial risk governance, cloud native regulationAbstract
The accelerating integration of artificial intelligence, cloud computing, and automated decision systems into financial and regulatory infrastructures has produced a fundamental transformation in how compliance, risk management, and accountability are conceptualized and operationalized. Traditional compliance frameworks were built on static rulebooks, manual audits, and retrospective accountability, whereas modern financial ecosystems operate through continuous, algorithmically mediated transactions that demand real time governance, traceability, and interpretability. This research addresses the growing tension between automation and accountability by examining how algorithmic compliance architectures can be designed to ensure regulatory integrity while preserving operational efficiency and institutional trust. Drawing on interdisciplinary literature from software engineering, financial compliance, explainable artificial intelligence, cloud infrastructure, and digital governance, the article develops a comprehensive theoretical and methodological framework for what is described as algorithmic compliance engineering.
A central contribution of this study is the conceptual integration of automated auditability, model interpretability, and regulatory traceability within cloud native machine learning pipelines. In particular, the study builds upon the emerging paradigm of compliance as executable code, in which regulatory constraints are embedded directly into machine learning workflows and cloud orchestration layers. The framework is grounded in recent advances in automated audit trails within cloud based machine learning environments, as demonstrated in the HIPAA as Code paradigm implemented in AWS SageMaker pipelines, which illustrates how compliance obligations can be rendered machine enforceable, continuously verifiable, and systematically auditable (2025. HIPAA-as-Code: Automated Audit Trails in AWS Sage Maker Pipelines, 2025). This approach is extended beyond healthcare to financial compliance, where similar requirements for data protection, fairness, traceability, and accountability exist, often with even greater economic and social consequences.
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Copyright (c) 2026 Quentin J. Fairchild
This work is licensed under a Creative Commons Attribution 4.0 International License.
Individual articles are published Open Access under the Creative Commons Licence: CC-BY 4.0.
