Population based Optimized and Condensed Fuzzy Deep Belief Network for Credit Card Fraudulent Detection

In this information era, with the advancement in technology, there is a high risk due to financial fraud which is a continually increasing menace during online transactions. Credit card fraudulent identification is a toughest challenge because of two important issues, as the profile of the credit card user's behavior changes constantly and credit card datasets are skewed. The factors which greatly affects the credit card fraudulent transaction detection are primarily based on data sampling models, features involved in feature selection and detection approaches implied. To overwhelm these issues, instead of using certainty theory, this paper encapsulates with three different empowered models are deployed for intellectual way of fraudulent transaction detection. In this work uncertainty theory of intuitionistic fuzzy theorem to determine the significant features which will influence the detection process effectively. Maximized relevancy among dependent and independent features of credit card dataset are determined using grade of membership and non-membership information of each features. The intuitionistic fuzzy mutual information with the knowledge of entropy it selects the features with highest information score as significant feature subset. This proposed model devised Fuzzy Deep Belief Network enriched with Sea Turtle Foraging for credit card fraudulent detection (EFDBN-STFA). The fuzzy deep belief network greatly handles the complex pattern of credit card transactions with its deep knowledge and stacked restricted Boltzmann machine the pattern of dataset is analyzed. The weights assigned to the hidden nodes are fine-tuned by the sea turtle foraging using its fitness measure and thus it improves the detection accuracy of the FDBN. Simulation results proved the efficacy of EFDBN-STFA on two different credit card datasets with its gained ability of handling hesitation factor and optimization using metaheuristic approach, it achieves higher detection rate with reduced false alarms compared to other existing detection models.