Predicting mobile money transaction fraud using machine learning algorithms

Author:

Lokanan Mark E.1ORCID

Affiliation:

1. Faculty of Management Royal Roads University Victoria Canada

Abstract

AbstractThe ease with which mobile money is used to facilitate cross‐border payments presents a global threat to law enforcement in the fight against money laundering and terrorist financing. This paper aims to utilize machine learning classifiers to predict transactions flagged as a fraud in mobile money transfers. The data for this study were obtained from real‐time transactions that simulate a well‐known mobile transfer fraud scheme. Logistic regression is used as the baseline model and is compared with ensemble and gradient descent models. The results indicate that the logistic regression model still showed reasonable performance while not performing as well as the other models. Among all the measures, the random forest classifier exhibited outstanding performance. The amount of money transferred emerged as the top feature for predicting money laundering transactions in mobile money transfers. These findings suggest that further research is needed to enhance the logistic regression model, and the random forest classifier should be explored as a potential tool for law enforcement and financial institutions to detect money laundering activities in mobile money transfers.

Publisher

Wiley

Subject

General Medicine

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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3. Refining Detection Mechanism of Mobile Money Fraud Using MoMTSim Platform;Communications in Computer and Information Science;2024

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