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

Ravi Kiran Alluri

doi:10.71097/IJAIDR.v10.i2.1518

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

Author(s)	Ravi Kiran Alluri
Country	United States
Abstract	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.
Keywords	Enterprise Data Warehouse, Fraud Detection, Rule-Based Systems, Machine Learning, Hybrid Detection Framework, Anomaly Detection, Data Analytics, Financial Fraud, Classification Algorithms, ETL Pipelines.
Field	Engineering
Published In	Volume 10, Issue 2, July-December 2019
Published On	2019-09-06
Cite This	Integrating Rule-Based and ML-Based Fraud Detection in Enterprise Data Warehouses - Ravi Kiran Alluri - IJAIDR Volume 10, Issue 2, July-December 2019. DOI 10.71097/IJAIDR.v10.i2.1518
DOI	https://doi.org/10.71097/IJAIDR.v10.i2.1518
Short DOI	https://doi.org/g9wpwf

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

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