  The UK anti-fraud charity Fraud Advisory Panel (FAP) in their review of 2016
estimates business costs of fraud at 144 billion, and its individual
counterpart at 9.7 billion. Banking, insurance, manufacturing, and government
are the most common industries affected by fraud activities. Designing an
efficient fraud detection system could avoid losing the money; however,
building this system is challenging due to many difficult problems,
e.g.imbalanced data, computing costs, etc. Over the last three decades, there
are various research relates to fraud detection but no agreement on what is the
best approach to build the fraud detection system. In this thesis, we aim to
answer some questions such as i) how to build a simplified and effective Fraud
Detection System that not only easy to implement but also providing reliable
results and our proposed Fraud Detection Pipeline is a potential backbone of
the system and is easy to be extended or upgraded, ii) when to update models in
our system (and keep the accuracy stable) in order to reduce the cost of
updating process, iii) how to deal with an extreme imbalance in big data
classification problem, e.g. fraud detection, since this is the gap between two
difficult problems, iv) further, how to apply graph-based semi-supervised
learning to detect fraudulent transactions.
