Abstract
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.