Solution Pattern for Anomaly Detection in Financial Data Streams

Abstract

Anomaly detection in versatile financial data streams is a vital business problem. Existing IT solutions for business anomaly detection usually rely on explicit Complex Event Processing or near-real time Business Activity Monitoring. In this paper we argue that business anomaly detection should be considered an implicit infrastructural BPM service and we propose a corresponding Solution Pattern. We describe how a Business Anomaly Detector can be architectured and designed in order to handle fast dynamic streams of business objects in BPM environments. The presented solution has been practically verified in Oracle SOA/BPM Suite environment which handled real-life financial controlling business processes.