Mobile money fraud detection using data analysis and visualization techniques

Financial investigations in the realm of fraud detection demand rigorous data analysis to identify anomalies and inform decision-making. This paper demonstrates the importance of data visualization as a means of conducting initial assessments of testable datasets to validate their suitability and promptly detect unexpected patterns before delving deeper into investigations. Using the publicly available PAYSIM dataset as a case study, we analyzed 6,362,620 records, of which 8213 were fraudulent and the remainder were legitimate. The dataset comprised 9 features and a single target class. Our analysis reveals the powerful role of visualization in identifying early indications of incompatibility with the dataset and guiding analysts to question its fitness for the context at hand. In particular, we show how visualization can highlight key findings and provide an added emphasis to the results. Through visual and numerical analysis, we demonstrate the importance of identifying potential outliers and other anomalies before proceeding with data preprocessing and modeling. Our results suggest that visual analysis of data is an essential step in detecting fraudulent activities in mobile money transactions. This approach can help to improve the accuracy and efficiency of fraud detection systems, thereby protecting users from financial losses. We conclude that data visualization should be an integral part of any data analysis project, especially in the field of fraud detection, to ensure the validity and suitability of the data before proceeding with further investigations.