DESIGN AND IMPLEMENTATION OF A CREDIT CARD FRAUD DETECTION SYSTEM
Abstract
A well-defined classification model is developed by recognizing the patterns of former fraudulent behaviors. Then, the model is used to predict any suspicious transaction implied in a new data set. Data mining and model construction require a lot of time, which prohibits it to detect frauds in real time. This is a serious setback since, in many occasions such as online credit card transactions, one need to detect fraudulent activities in a very short period of time, typically while the fraudster is still at the banking hall. Otherwise, the loss could be huge.
Multi-agents are computer programs that can act on behalf of a person to do various jobs. Multi- agents can automate a large portion of fraud detection process and require little human intervention. Additionally, multi-agents do not stick to one model or rule. They can construct new models and rules for fraud detection with their machine capabilities. It will be harder to deceive multi- agents than other computer programs for fraud detection. Besides, in a multi- agents system, many multi-agents can work in parallel and cooperate with each other. This not only accelerates the detection process but also increases the detection accuracy. Moreover, multi-agents can be deployed online for real-time detection. This is an extremely desirable feature for online credit card fraud detection and network intrusion detection.
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