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.