The Watchful Guardian That Never Says “Pause”: A Self‑Supervised AI Framework for Real‑Time Compliance Auditing in High‑Velocity Fintech MLOps

Satyanarayana Gopisetty Journal

Authors

Satyanarayana Gopisetty Journal Frisco Texas, United States Author

Keywords:

Self-supervised learning, Real-time compliance auditing, Fintech MLOps, Continuous integration and delivery (CI/CD), Regulatory technology (RegTech), Explainable AI for audit trails, Human-in-the-loop oversight, High-velocity machine learning deployment

Abstract

In fintech, machine learning models are updated and deployed constantly—sometimes multiple times a day. Meanwhile, financial regulations demand that every change be audited, explained, and approved. These two realities clash. Today, the only way to satisfy regulators is to slow down or even stop the pipeline for manual checks, which defeats the purpose of using DevOps and MLOps for speed. This paper proposes a different solution: a self‑supervised artificial intelligence framework that acts like a tireless, silent guardian. It watches every step of the merged DevOps/MLOps pipeline in real time model training, versioning, deployment, rollbacks, and drift corrections. Without ever asking the pipeline to pause, it learns what normal, compliant behavior looks like. When something violates a regulatory rule (for example, a model update missing an explainability report or a rollback without a proper audit trail), the framework flags it instantly and automatically generates a plain‑language, regulator‑friendly explanation of what happened, when, and why. The framework does not replace human auditors; it empowers them by doing the tedious, continuous watching for them. We demonstrate that this approach catches compliance violations faster and more thoroughly than periodic manual audits, all while allowing the fintech pipeline to run at full speed. The result is a system that respects both machine speed and human rules without ever having to say “pause.”

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