Self-Healing Data Pipelines: AI-Driven Automation in Financial Data Operations

Abstract

Net asset value calculation, treasury reporting, and risk aggregation all run on data pipelines that are expected to work every day, on time, without exception. In practice they don’t always. Upstream feeds arrive late. Source schemas change without warning. Null rates spike. Replication channels back up under load. Static-threshold monitoring catches the obvious cases and misses the rest, and it treats a market-wide price swing the same way it treats a corrupted feedvas an alert to be triaged manually. This paper proposes a self-healing pipeline architecture built around four separated, auditable stages: telemetry collection, machine learning-driven anomaly detection, dependency-aware diagnosis, and governed remediation. The detection logic draws directly from production price-validation practicevchecking a security’s movement against correlated peers rather than judging it alone because that distinction is what separates a real anomaly from ordinary market behavior. Evaluation combines synthetic fault injection with replay of historical incidents, and reports verified production evidence: dependency-aware filtering of NAV pricing exceptions reduced false-positive review volume by roughly 95%, and eliminating redundant source-side updates cut change-data-capture replication lag from hours to seconds. Detection and recovery speed are characterized qualitatively rather than with an invented figure, since no verified time-to-detect or time-to-recover statistic exists for the reference production system. The paper closes with a look at generative-AI-assisted remediation reporting and regulatory-aware automation as next steps.