Unifying AI and Rule-based Models for Financial Fraud Detection

Financial fraud has become increasingly sophisticated, necessitating a blend of traditional and modern technologies to combat it effectively. This paper explores integrating rules-based systems with Artificial Intelligence (AI) models, especially machine learning techniques, to detect, prevent, and mitigate financial fraud. Through a comprehensive literature review, this study evaluates existing fraud detection techniques, compares their strengths and weaknesses, and proposes a hybrid approach that leverages historical rules and data-driven AI insights. Real-world use cases are analyzed to demonstrate how combining these approaches can result in more accurate fraud detection with fewer false positives. The findings offer strategic insights for organizations seeking to enhance banking, insurance, and financial fraud detection systems. Building a fraud prevention framework often exceeds creating a highly accurate machine learning (ML) model due to an ever-changing landscape and customer expectations. Oftentimes, it involves a complex ETL process with a decision science setup that combines a rules engine with an ML platform. The requirements for such a platform include scalability and isolation of multiple workspaces for cross regional teams built on open-source standards. By design, such an environment empowers data scientists, engineers and analysts to collaborate securely. We will first look at using a data Lakehouse architecture combined with Databricks’ enterprise platform, which supports the infrastructure needs of all downstream applications of a fraud prevention application. This paper will reference Databricks’ core components of Lakehouse called Delta Engine, a high-performance query engine designed for scalability and performance on big data workloads, and MLflow, a fully managed ML governance tool to track ML experiments and quickly productize them.