Predictive Maintenance for Compliance Systems using Machine Learning

Abstract

Predictive maintenance uses machine learning to drive the revolution in compliance system management by taking proactive measures that keep a system from failing. Most of the traditional strategies adopted in maintenance, such as reactive and preventive, result in several inefficiencies, unexpected plant downtime, and highly costly penalties due to regulatory non-compliance. Integration of ML algorithms will help organizations perform real-time anomaly detection, predict impending failures, and act in time to maintain continued compliance and operational reliability. This paper examines predictive maintenance used in compliance systems and performances that have been improved because of ML-driven insights, reducing maintenance costs. Further, the paper shows simulated scenarios on applications of ML models to identify risks across several domains: financial reporting, industrial safety, and medical device monitoring. Key challenges- data quality, model scalability, and interpretability- are discussed, coupled with solutions for identifying and mitigating such pitfalls. Predictive maintenance, driven by ML, will be a key enabler in ensuring a resilient and efficient compliance management framework.