As shown in Table 7, the decision tree classifier had the highest precision score, which means that the decision tree model did an excellent job predicting the fraudulent transactions. Note from Figure 5 that the type of transfer was the feature selected to split the tree. If the amount laundered is ≤0.5, the tree splits at the amount that was cashed out, and if the amount is ≥ 0.5, the tree splits at the amount the recipient had after the laundered transaction (newbalanceDest). The ‘type of transfer’ is an important feature in classifying a money laundering transaction because it can show how sophisticated or structured the laundering process is. For example, a cash-out is a transfer with an influx of money into one account and then an immediate withdrawal of those funds. This type of laundering is generally associated with low-level or first-time offenders.
In contrast, a structured deposit is when funds are gradually deposited into an account over time before being withdrawn. This type of laundering is generally associated with more knowledgeable or experienced offenders with access to multiple accounts. The decision tree correctly classified 84% of all cash-outs as money laundering transactions and 100% of all structured deposits as money laundering transactions. These results indicate that the decision tree is an effective classifier for identifying money laundering activities. Both the accuracy scores for cash-outs and structured deposits are high, suggesting that the decision tree could be improved by adding more information, such as the customer’s location or account history.