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Title: A widespread survey on machine learning techniques and user substantiation methods for credit card fraud detection

Authors: T. John Berkmans; S. Karthick

Addresses: Department of Computer Science Engineering, SRM Institute of Science and Technology (SRMIST), Kattankulathur, Chennai, India ' Department of Computational Intelligence, SRM Institute of Science and Technology (SRMIST), Kattankulathur, Chennai, India

Abstract: In this modern scientific digital world, credit card usage has enormously increased everyday. Simultaneously a huge amount of credit card misuse also has been expressively popular. It prompts monetary misfortunes for both charge cardholders and monetary associations. To keep away from that, monetary associations created and conveyed Visa extortion discovery techniques. In the upcoming years, everybody will utilise the greatest exchange through online mode just to save their time. So we partition this review into two primary parts. In the first part, we centre around old-style AI models, and in this part we focus on what the client knows (knowledge-based strategy). For the second part, we focus more on the turn of events procedure of client verification, and their conduct biometrics to distinguish an individual remarkable conduct while utilising their electronic gadgets. An outline of the current methodology in this writing review means to grow a more precise, dependable, versatile, super-fast, effective, and modest model of charge card extortion identification.

Keywords: credit card transaction; machine learning; bio-metrics; XGBoost; SVM; random forest.

DOI: 10.1504/IJBIDM.2023.127325

International Journal of Business Intelligence and Data Mining, 2023 Vol.22 No.1/2, pp.223 - 247

Received: 08 Sep 2021
Accepted: 28 Dec 2021

Published online: 30 Nov 2022 *

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