Ph.D. (Computer Science) / Thanks to several advancements in communication technologies, the world today is a highly connected society promoting business transformations that highlight improved efficiency [1]. Unfortunately, systems developed for an increasingly connected world are also subject to increases in change, complexity and risk – the same connectedness that makes lives easier also signifies that any negative influences can be more difficult to handle and contain [2]. Multi-agent systems have been touted as ideal solutions to realising the required complexities across wide and varied problem domains that range from manufacturing [3] to eco-system management [4] to construction [5]. In an increasingly connected world, complex problems may require that various multi-agent systems work together in order to accomplish larger, overarching objectives. A fraud detection system, for example, could comprise a number of multi-agent systems, each designated to fulfil a very specific and important fraud detection task. The success of the fraud detection system will then depend on each of the various multi-agent systems’ abilities to achieve allocated goals and thus, contribute towards efforts to detect fraud accurately. Depending on factors that include objective and environment type, fraud detection tasks may entail working with numerous disparate systems [6] – it is possible that agent designs that are different from the rest of the fraud detection system must be implemented.Such inconsistency between multi-agent systems could potentially lead to conflicting goals, thereby jeopardising the resolution of the fraud detection system’s overall objectives. A further complication that may arise is the continuously changing financial services landscape – fraud detection systems must not only contend with the creativity of fraudsters, but should also be acutely aware of when day-to-day processes have changed due to recent innovations or technological advancements in the domain. Existing fraud detection methodologies may therefore need to be updated frequently in order to remain sufficiently informed of current developments. An agent-based fraud detection model was thus developed to assist anti-fraud professionals in the classification of day-to-day financial transactions. The proposed model comprises a number of multi-agent systems, each incorporated to add a particular aspect of the criminal justice process in investigating incidences of potential crime. By having agents emulate the various tasks that are involved in dealing with a crime, it is anticipated that the resulting fraud detection system will be able to achieve similar successes from applying the same procedure. In order to successfully develop the fraud detection model, an architecture for implementing a collaborative community of multi-agent subsystems for a dynamic environment was also developed. The architecture is intended to allow each multi-agent subsystem member to adapt to changes in the environment while ensuring that teamwork links are maintained amongst the different subsystems.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uj/uj:9251 |
| Date | 14 August 2012 |
| Creators | Leung, Wai Sze |
| Source Sets | South African National ETD Portal |
| Detected Language | English |
| Type | Thesis |
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