Figure 3: Types of Transfer
Analytical Results
Baseline Logistic Regression
In this study, we sought to compare the performances of several
different classifiers to determine which would be best suited for
predicting financial transaction fraud. Logistic regression was used as
the base model, then compared with the results of other classifiers. As
shown in Figure 4, all the features except cash-in, debit, and payment
type are statistically significant at a p-value of 0.05 and with
a confidence interval of 95%. These findings suggest that the other
classifiers may be more accurate in predicting fraud than the logistic
regression model 25. More research is needed, but the
logistic regression results suggest that other classifiers should be
considered when predicting financial transaction fraud. To build the
different classifiers and to see how the model performs using the
classification metrics precision, recall, and the F1-score, the features
with a p-value > 0.05 will be dropped from the
dataset.