Integrating Rule-Based and ML-Based Fraud Detection in Enterprise Data Warehouses

The increasing complexity and volume of enterprise transactional data have made fraud detection a critical concern for organizations that rely on large-scale data warehousing systems. Traditional rule-based fraud detection techniques, which define static thresholds, if-else logic, and deterministic conditions, have long served as the backbone of fraud prevention in financial institutions, telecommunications, e-commerce, and other data-intensive domains. However, as fraudsters adopt more sophisticated and adaptive strategies, these rule-based systems exhibit limitations in flexibility, scalability, and adaptability to unseen patterns. Conversely, machine learning (ML)-based approaches offer the potential to identify previously unknown fraud patterns by learning complex relationships and anomalies from historical data. Yet, their integration into enterprise environments remains challenging due to issues such as interpretability, data governance, regulatory constraints, and the need for rigorous validation before deployment.