Wallet-Based Transaction Fraud Prevention Through LightGBM With the Focus on Minimizing False Alarms

Abstract

E-wallets' rising popularity can be attributed to the fact that they facilitate a wide variety of financial activities such as payments, transfers, investments, etc., and eliminate the need for actual cash or cards. The confidentiality, availability, and integrity of a user's financial information stored in an electronic wallet can be compromised by threats such as phishing, malware, and social engineering; therefore, fintech platforms employ intelligent fraud detection mechanisms to mitigate the problem. The purpose of this study is to detect fraudulent activity using cutting-edge machine learning techniques on data obtained from the leading e-wallet platform in Turkey. After a comprehensive analysis of the dataset's features via feature engineering procedures, we found that the LightGBM approach had the highest detection accuracy of fraudulent activity with 97% in the experiments conducted. An additional key objective of reducing false alerts was accomplished, as the number of false alarms went from 13,024 to 6,249. This approach resulted in the establishment of a machine-learning model suitable for use by relatively small fraud detection teams.