Abstract: Suspicious transactions in mobile money systems have become a growing concern due to the increasing volume of digital transactions and the sophistication of fraudulent activities. Mobile money platforms, widely used for quick and secure financial transfers, are vulnerable to various types of fraud, such as identity theft, unauthorized access, and transaction manipulation. Detecting fraudulent transactions in real-time is a challenge due to the vast amounts of data and the dynamic nature of fraudulent behaviors. Existing systems often struggle with high false-positive rates or fail to catch advanced fraud patterns, leading to financial loss and security breaches. This paper proposes a secure model for mobile money applications, incorporating two layers of authentication that include passwords, One-Time Passwords (OTP), and a machine learning-based approach. Specifically, the Random Forest classifier is employed to accurately detect suspicious mobile money transactions. Results show that the system ensures a robust and scalable solution to efficiently identify fraudulent transactions with minimal user intervention, achieving 100% training accuracy and 99% testing accuracy, enhancing financial security. This innovation plays a crucial role in safeguarding mobile money platforms globally.
Keywords: Mobile money, One-Time Passwords, Random Forests, Suspicious Transactions
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DOI:
10.17148/IJARCCE.2025.14215
