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Predicting Mobile Money Transaction Fraud using Machine Learning Algorithms
  • Mark Lokanan
Mark Lokanan
Royal Roads University Faculty of Management

Corresponding Author:[email protected]

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The ease with which mobile money is used to facilitate cross-border payments presents a global threat to law enforcement in the fight against laundering and terrorist financing. This paper aims to use machine learning classifiers to predict transactions flagged as a fraud in mobile money transfers. Data for this paper came from real-time transactions that stimulate a  well-known mobile transfer fraud scheme. This paper uses logistic regression as the baseline model and compares it with ensembles and gradient descent models. The results indicate that the established logistic regression model did not perform too poorly compared to the other models. The random forest classifier had the most outstanding performance among all measures. The amount of money transferred was the top feature to predict money laundering transactions through mobile money transfers. These findings suggest that more research is needed to improve the logistic regression model. The random forest classifier should be further explored as a potential tool for law enforcement and financial institutions to detect money laundering activities in mobile money transfers.
15 Apr 2023Submitted to Applied AI Letters
17 Apr 2023Submission Checks Completed
17 Apr 2023Assigned to Editor
18 Apr 2023Reviewer(s) Assigned
10 May 2023Review(s) Completed, Editorial Evaluation Pending
10 May 2023Editorial Decision: Revise Major
05 Jun 20231st Revision Received
09 Jun 2023Submission Checks Completed
09 Jun 2023Assigned to Editor
12 Jun 2023Reviewer(s) Assigned
14 Jun 2023Review(s) Completed, Editorial Evaluation Pending
19 Jun 2023Editorial Decision: Accept