An Intelligent Fraud Detection Model based on Deep Learning Ensembles

Abstract Fraud detection and prevention has received a lot of attention from the research community due to its high impact on financial institutions' revenues and reputation. The increased use of the Web and the provision of online services open up the pathway for exposing these systems to numerous threats and jeopardise their effective functioning. Naturally, financial frauds are increased in number and forms imposing various requirements for their efficient and immediate detection. These requirements are related to the performance of the adopted models as well as the timely response of the decision making mechanism. Machine Learning (ML) and data mining are two research domains that can provide a number of techniques/algorithms for fraud detection and setup the road for mitigation actions. However, these methods still need to be improved with respect to the detection of unknown fraud patterns and the incorporation of big data processing mechanisms. This paper presents our attempt to build a hybrid system, i.e., an ensemble scheme for combining two deep learning models and efficiently detect potential financial frauds. We elaborate on the combination of an autoencoder and a Long Short-Term Memory (LSTM) recurrent neural network trained upon datasets which are processed through the use of an oversampling technique. Oversampling is adopted to handle heavily imbalanced datasets which is the `natural' scenario due to the limited number of frauds compared to the humongous volumes of transactions. The proposed approach tends to capture much more fraud events in comparison with other conventional ML techniques. Our experimental evaluation exposes that our model exhibits a good performance in terms of accuracy, recall & precision.