DEEP LEARNING-BASED FRAUD DETECTION IN FINANCIAL TRANSACTIONS

Abstract

Detection of credit card frauds is a significant activity in the current financial systems with the rates of electronic transactions escalating and the fraudsters upgrading their fraud techniques as they continually develop. The fact that it is also extremely imbalanced with fraudulent transactions making only a small fraction of the overall data only exacerbates the problem. Hence, there is a need to have effective and smart mechanisms of detection that will guarantee the security of transactions and minimize losses incurred due to financial changes. The objective of the paper is to create and test a deep learning-based credit card fraud detector with the help of Artificial Neural Networks and convolutional neural networks architecture. To meet this goal, a real-life credit card transaction dataset was utilized and until a number of data pre-processing methods were employed that comprised; data normalization, dealing with data imbalances with Synthetic Minority Oversampling Technique, and dimensionality reduction with Principal Component Analysis. The practical section of the paper is based on the implementation and the comparison of three deep learning models, namely, Artificial Neural Network, VGG16, and VGG19. The models were trained and evaluated by normal performance measures which were accuracy, precision, recall and F1-score. It has been experimentally demonstrated that Artificial Neural Networks model depicted the greatest detection precision of 99%, more than both the VGG16 and VGG19 models. These findings validate that deep learning techniques can be used to detect fraudulent credit card transactions and deployed in real time systems of fraud detection.