Detecting suspicious transactions in credit cards using the Extra Trees Classifier algorithm
Detecting suspicious transactions in credit cards using the Extra Trees Classifier algorithm
Subject Areas : Financial Knowledge of Securities Analysis
Erfan Abedini 1 , Mahmmoud Hematfar 2 *
1 - Master's Student, Department of Financial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 - Associate Professor, Department of Accounting, Boroujerd Branch, Islamic Azad University, Boroujerd, Iran (Corresponding Author)
Keywords: Machine learning, machine learning in fraud detection, credit card fraud, online fraud, credit card fraud.,
Abstract :
In recent years, with the development of e-commerce, credit cards have been widely used in transactions for various reasons, including the ease and efficiency they provide. Fraud and unauthorized transactions with credit cards have become a common and significant problem due to the increased use of credit cards. Therefore, financial institutions bear a significant responsibility to update their existing mechanisms to prevent such unauthorized actions. The lack of a specific pattern and an imbalance in the number of authorized and unauthorized transactions have posed challenges in developing fraud detection models and systems. The main objective of this study is to create a suitable, accurate, and fast solution for fraud detection in credit cards based on machine learning. The proposed model in this article includes the use of the SMOTE method to handle imbalanced data and the Extra Trees Classifier algorithm to identify unauthorized transactions. Various metrics examining accuracy and performance indicate the effective performance of the proposed model.
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