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.