• sezburg scott peter mkunyah

With the increasing popularity of Mobile Money Transaction Services adopted in developing countries, there has been a corresponding rise in fraud and money laundering cases. Therefore, there should be a modern and effective way for combating fraudulent activities with the new technological innovation techniques . This study paper will come up with effective Machine Learning model for detecting and predicting fraud and any suspicious transaction activities in mobile money. The study will adopt comprehensive and Case-Based -Reasoning methodologies, The comprehensive methodology will divide literature review into various stages includes mobile money background and mobile money ecosystem ,machine learning theory, research experimental design and implementation of the model which covers data collection , data prepossessing, exploratory data analysis data modelling, machine learning model classification algorithms like Logistic regression, random forests, gradient descent and also model evaluation using the Receiver Operating Characteristic (ROC) AUC (Area Under Curve) and F1-Score . The findings indicate that using supervised machine learning especially logistic regression algorithm can effectively help to detect fraud in mobile money transaction services and make mobile money service be among the tools that improve fair trade in financial sectors on developing countries that adopt the mobile money services. Through discoveries in the experiment demonstrated and illustrated, this paper shows how Machine Learning algorithms can be used to predict mobile money transaction

Receiver Operating Characteristic (ROC) AUC (Area Under Curve)

"Predicting Mobile Money Transaction Fraud Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 1, page no.a305-a313, January-2025, Available :http://www.jetir.org/papers/JETIR2501037.pdf

"Predicting Mobile Money Transaction Fraud Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 1, page no. ppa305-a313, January-2025, Available at : http://www.jetir.org/papers/JETIR2501037.pdf