Towards a Proactive Fraud Management Framework for Financial Data Streams

Effective and efficient fraud prevention is a core capability required from financial institutions towards detecting and minimizing losses due to unlawful transactions. With the ubiquitous availability of unmanned customer interaction channels (e.g., Internet and mobile banking), the challenge of controlling fraud has increased substantially demanding fraud management frameworks capable of providing fraud analysts with effective mechanisms for defining fraud policies, and system architectures for large scale real-time screening of click stream data, arising from multiple channels at differing time windows. In this paper we describe a fraud management framework encompassing a rule-based financial fraud modelling language (FFML) for conceptual level modelling and validation of fraud policies and a fraud prevention architecture based on implementing fraud policies using StreamSQL, a novel and emerging standard for processing real-time data streams. A key element of the framework is the attempt to detect fraud proactively, blocking transactions encompassing suspicious click stream patterns. The framework described in this paper is being developed as an integral part of the FSA compliance program within the SpartaPay payment gateway, a multi-channel financial platform for managing micropayments.