DeLClustE: Protecting Users from Credit-Card Fraud Transaction via the Deep-Learning Cluster Ensemble

Fraud is the unlawful acquisition of valuable assets gained via intended misrepresentation. It a crime committed by either an internal/external user, and associated with acts theft, embezzlement, larceny. The proliferation credit cards to aid financial inclusiveness has its usefulness alongside it attracting malicious attacks for gains. Attempts classify fraudulent card transactions have yielded formal taxonomies as these seek evade detection. We propose deep learning ensemble profile hidden Markov model neural network, which poised effectively credit-card fraud high degree accuracy, reduce errors, timely fashion. result shows classified benign precision 97 percent. Thus, we posit new scheme that more logical, intuitive, reusable, exhaustive, robust in classifying such based on attack source, cause(s), time gap.