Abstract: The goal of data analytics is to delineate hidden patterns and use them to support informed decisions in a variety of situations. Credit fraud is escalating significantly with the advancement of modernized technology and became an easy target for frauds. Credit fraud has highly imbalanced publicly available datasets. In this paper, we apply supervised machine learning algorithms to detect credit card fraudulent transactions using a real-world dataset. Furthermore, we employ these algorithms to implement a super classifier using ensemble learning methods. The software identifies faces which are already recognized and automatically adds the credit points to the customer’s account. If the customer is not found, then the system requests the customer picture and contact details for customer to be added to the database. The points can be redeemed as the company pleases. Additionally, we compare and discuss the performance of various supervised machine learning algorithms that exist in literature against the super classifier that we implemented in this paper. Finally, we design and assess a prototype of a fraud detection system able to meet real-world working conditions that is able to integrate investigators feedback to generate accurate alerts.
Keywords: Credit Card, Fraud detection, supervised machine learning, face recognisation Classification, Imbalanced dataset, Sampling.
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DOI:
10.17148/IJARCCE.2022.115103
