Computer-aided financial fraud detection: Promise and applicability in monitoring financial transaction fraud

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

A knowledge-intensive approach to process similarity calculation

Process model comparison and similar processes retrieval are key issues to be addressed in many real world situations, and particularly relevant ones in some applications (e.g., in medicine), where similarity quantification can be exploited in a quality assessment perspective. Most of the process comparison techniques described in the literature suffer from two main limitations: (1) they adopt a purely syntactic (vs. semantic) approach in process activity comparison, and/or (2) they ignore complex control flow information (i.e., other than sequence). These limitations oversimplify the problem, and make the results of similarity-based process retrieval less reliable, especially when domain knowledge is available, and can be adopted to quantify activity or control flow construct differences. In this paper, we aim at overcoming both limitations, by introducing a framework which allows to extract the actual process model from the available process execution traces, through process mining techniques, and then to compare (mined) process models, by relying on a novel distance measure. The novel distance measure, which represents the main contribution of this paper, is able to address issues (1) and (2) above, since: (1) it provides a semantic, knowledge-intensive approach to process activity comparison, by making use of domain knowledge; (2) it explicitly takes into account complex control flow constructs (such as AND and XOR splits/joins), thus fully considering the different semantic meaning of control flow connections in a reliable way. The positive impact of the framework in practice has been tested in stroke management, where our approach has outperformed a state-of-the art literature metric on a real world event log, providing results that were closer to those of a human expert. Experiments in other domains are foreseen in the future

Intelligent monitoring of business processes using case-based reasoning

The work in this thesis presents an approach towards the effective monitoring of business processes using Case-Based Reasoning (CBR). The rationale behind this research was that business processes constitute a fundamental concept of the modern world and there is a constantly emerging need for their efficient control. They can be efficiently represented but not necessarily monitored and diagnosed effectively via an appropriate platform. Motivated by the above observation this research pursued to which extent there can be efficient monitoring, diagnosis and explanation of the workflows. Workflows and their effective representation in terms of CBR were investigated as well as how similarity measures among them could be established appropriately. The monitoring results and their following explanation to users were questioned as well as which should be an appropriate software architecture to allow monitoring of workflow executions. Throughout the progress of this research, several sets of experiments have been conducted using existing enterprise systems which are coordinated via a predefined workflow business process. Past data produced over several years have been used for the needs of the conducted experiments. Based on those the necessary knowledge repositories were built and used afterwards in order to evaluate the suggesting approach towards the effective monitoring and diagnosis of business processes. The produced results show to which extent a business process can be monitored and diagnosed effectively. The results also provide hints on possible changes that would maximize the accuracy of the actual monitoring, diagnosis and explanation. Moreover the presented approach can be generalised and expanded further to enterprise systems that have as common characteristics a possible workflow representation and the presence of uncertainty. Further work motivated by this thesis could investigate how the knowledge acquisition can be transferred over workflow systems and be of benefit to large-scale multidimensional enterprises. Additionally the temporal uncertainty could be investigated further, in an attempt to address it while reasoning. Finally the provenance of cases and their solutions could be explored further, identifying correlations with the process of reasoning

A case based reasoning approach for the monitoring of business workflows

This paper presents an approach for the intelligent diagnosis and monitoring of business workflows based on operation data in the form of temporal log data. The representation of workflow related case knowledge in this research using graphs is explained. The workflow process is orchestrated by a software system using BPEL technologies within a service-oriented architecture. Workflow cases are represented in terms of events and their corresponding temporal relationships. The matching and CBR retrieval mechanisms used in this research are explained and the architecture of an integrated intelligent monitoring system is shown. The paper contains an evaluation of the approach based on experiments on real data from a university quality assurance exam moderation system. The experiments and the evaluation of the approach is presented and is shown that a graph matching based similarity measure is capable to diagnose problems within business workflows. Finally, further work on the system and the extension to a full intelligent monitoring and process optimisation system is presented