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This is a loaded question with deep answers and varied methods. The problem with fraud detection is that it becomes a behavioural detection and decision making. Basically, this becomes a classification problem where the machine is taught certain behaviours based on (for example) credit card usage. There are many methods to apply here to the data obtained from a collection of data or to live stream of data where machine learning and AI are monitoring and making decisions on the incoming data based on the learned model and adjusting it as it builds in the machine’s database. The goal is to give a go or no-go for specific transactions where correct transactions are permitted and fraudulent ones are stopped without having clients be lost because of false positives and also not to have client confidence decline because of lack of good detection. The significance of imbalanced data is huge; it permits many algorithms to be applied in order to resolve the machine learning process. For example, the use of Oversampling, Undersampling, ID3, CART, … etc. enables us; based on the data used and criteria of search, to enhance the accuracy and precision of the detection. In few papers, it was seen that Bayesian classifiers perform even better than the aforementioned methods with data to back it up. Regardless of the algorithm used, fraud detection is a problem of imbalanced data where the computer is detecting behaviour anomalies within an existing set of data or a stream of incoming data. I hope this was helpful.
