Anomaly detection in Rapidminer AI Studio: A comparative approach using DBSCAN clustering and isolation forest (PyOD)

The global banking industry is undergoing a significant digital transformation fueled by the widespread adoption of online and mobile banking services. While this evolution offers increased convenience and accessibility for consumers, it also exposes financial institutions to growing risks such as fraudulent transactions, irregular account activity and compliance violations. Traditional rule-based systems and batch-processing approaches, which rely on predefined thresholds and historical fraud patterns, are increasingly inadequate in detecting subtle or previously unseen anomalies, especially in real time.

RapidMiner® AI Studio offers a novel approach employing unsupervised machine learning techniques including density-based spatial clustering of applications with noise (DBSCAN) and Isolation Forest from the Python outlier detection (PyOD) library to better detect anomalies. Download the white paper to learn how machine learning models can be practically applied to address real-world challenges in financial operations.