Optimizing Regulatory Compliance with Machine Learning: Boosting Accuracy and Efficiency

Organizations face enormous difficulties in regulatory compliance across industries including banking, healthcare, and manufacturing as a result of manual processes leading to high error rates, costly penalties, and long delays in operation. Current approaches for managing the complexity of regulation through traditional rule-based systems and anomaly detection across vast archives of audit records do not meet the needs of these organizations. In this paper, I describe a new ML pipeline that includes an XGBoost classifier, BERT transformer for NLP, and Isolation Forest for anomaly detection using Snowflake's ML functions and Apache Spark's GPU clusters to allow for easy scalability. The ML pipeline automates the data import process from large datasets of compliance data, creates tokens and features, and allows for very fast in-database inference with extremely low latency. The ML pipeline includes MLflow for tracking model performance and feedback. The ensemble model has demonstrated exceptional performance with respect to both accuracy and efficiency; reduced processing time; and provided significant financial benefits for organizations that applied it in a number of case studies. The deployment of the ML pipeline provides for governance and explainability through the use of Snowflake Horizon and changes compliance from a reactive liability to being an active asset. Plans for the future include improving regulatory interpretation and scalability through the use of federated learning. Keywords: Rule-based Systems, Anomaly Detection, XGBoost Classifier, BERT Transformer, Snowflake Horizon