A voting-based hybrid machine learning approach for fraudulent financial data classification / Kuldeep Kaur Ragbir Singh - Kuldeep Kaur , Ragbir Singh (2019)
Credit card fraud is a growing concern in the financial industry. While financial losses from credit card fraud amount to billions of dollars each year, investigations on effective predictive models to identify fraud cases using real credit card data are limited currently, mainly due to confidentiality of customer information. To bridge this gap, this research embarks on developing a hybrid machine learning approach to identify credit card fraud cases based on both benchmark and real-world data. Standard base machine learning algorithms, which include a total of twelve individual methods as well as the AdaBoost and Bagging methods, are firstly used. The voting-based hybrid approach consisting of various machine learning models with the ability to tackle issues related to missing and imbalanced data is then developed. To evaluate the efficacy of the models, publicly available financial and credit card data sets are evaluated. A real credit card data set from a financial institution is also analysed, in order to evaluate the effectiveness of the proposed hybrid approach. In addition to the standard hybrid approach, a sliding window method is further evaluated using the real-world credit card data, with the aim to simulate and assess the capability of real-time identification of fraud cases at the financial institution. The empirical results positively indicate that the hybrid model with the sliding window method is able to yield a good accuracy rate of 82.4% in detecting fraud cases in real world credit card transactions.