Detection of credit card fraud transactions using machine learning algorithms techniques with data driven approaches: a comparative study

Finance fraud is a growing problem with far consequences in the financial industry and while many techniques have been discovered. It becomes challenging due to two major reasons–first, the profiles of normal and fraudulent behaviors change frequently and secondly due to reason that credit card fraud data sets are highly skewed. Credit card fraud events take place frequently and then result in huge financial losses [1]. The number of online transactions has grown in large quantities and online credit card transactions hold a huge share of these transactions. This paper focuses on four main fraud occasions in real-world transactions. Each fraud is addressed using a series of machine learning models and the best method is selected via an evaluation. This evaluation provides a comprehensive guide to selecting an optimal algorithm with respect to the type of the frauds and we illustrate the evaluation with an appropriate performance measure. Another major key area that we address in our project is real-time credit card fraud detection. For this, we take the use of predictive analytics done by the implemented machine learning models and an API module to decide if a particular transaction is genuine or fraudulent. This paper investigates and checks the performances of techniques are applied on the raw and preprocessed data. The performance of the techniques is evaluated based on accuracy, sensitivity, specificity and precision. In this paper, we will present a comparative study of some machine learning techniques, which gave the best results, according to our state of art [1] but applied to the same set of data. The objective of this study is to choose the best credit card fraud detection techniques to implement in our research work.