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into an account over time before being withdrawn.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.