Computer-aided Financial Fraud Detection: Promise and Applicability in Monitoring Financial Transaction Fraud

Georgios Samakovitis, Stelios Kapetanakis

Abstract


Anti-money Laundering (AML) and Financial Fraud Detection (FFD) have been receiving increasing attention in the past few years, especially in light of the global financial crisis. Closer systems integration and a number of latest steep technological developments in areas like Big Data; High Frequency Trading; e-payments; and mobile payment systems, to name a few, are now promising enhanced risk management through superior decision support for the global financial industry. At the same time, however, resident regulatory frameworks, national and international, appear to lack the connectivity and flexibility required to support integrated AML and FFD approaches. This is strongly testified by the disparate technological approaches to FFD across different Financial Institutions and their reluctance to share practice within the industry.

Focusing on Financial Transaction Fraud, this paper draws on the authors’ past research work which presented a prototype system that uses a workflow approach to identify abnormal financial transactions and applies Artificial Intelligence for classification. That work has shown successful applicability at short scale experiments, limited by the wide concern that information sharing should be achieved within the broader sector in order to achieve improved results. Drawing from there, this paper proposes that extending that approach across transaction infrastructure will deliver higher quality intelligent monitoring against Financial Transaction Fraud.

Following from that, we argue that the necessary technological maturity does exist to support full-scale operable FFD systems working on large disparate datasets. We then discuss the evidence in favour of the view that such systems can only be realised in the presence of wider regulatory consensus. There is, therefore, the need for a framework within which the technical infrastructure, business architecture and regulatory rules will harness that technological capability to deliver superior fraud prevention.

The paper first reviews computer-aided techniques and approaches for FFD available to the financial sector and discusses the business value of their application. It then addresses the main impediments for their full-scale applicability and uses an analytical framework for assessing their significance, in technological, business-specific and regulatory terms. A brief account of the authors’ workflow-based approach is then provided and its capabilities are outlined.

In light of the above analysis, the paper proposes a techno-economic framework that will facilitate delivery of unified knowledge from large and disparate data sets of financial transactions. That, we propose, will augment fraud reduction capabilities and contribute to significantly lower associated costs.

Keywords: Financial fraud detection, Anti-money laundering, Transaction monitoring, Artificial intelligence


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