Figure 6 : Results from the ROC Curve
Feature Importance
The features with the highest p-values in the baseline logistic regression model were amount, oldbalanceDest, newbalanceDest, cash-out, and transfer. These features were compared with the features from the ensemble classifiers to examine which ones contributed more to predicting laundered transactions. As shown in Figure 7, the amount involved in the transfer was again the top feature to predict suspicious transactions in mobile money transfers. These findings suggest that the amount of a transaction is a good predictor of whether a transaction is suspicious or not. The other features with p -values > 0.05 did not contribute much to predicting suspicious transactions. Altogether, mobile money transfer providers need to be aware of transfers involving large amounts of money and those made frequently or without a specific destination 30. By considering these factors, mobile money providers can detect and prevent suspicious behaviour on their platforms.