# Credit Card Fraud Detection in Card-Not-Present Transactions: Where to Invest?
## 5 Methodology
### Richer Features vs. More Complex Models  
This section explores how particular aspects of the developed new models contribute to improved performance. Specifically, we addressed the following research questions:  
1. How large is the difference between the baseline RE model and the developed models?
2. How much performance is gained by switching from a linear to a nonlinear model?
3. How much performance can be gained by including aggregated features in addition to trans features?  
To this end, we tested the models in the best performing scenario--the C model position and 5.sm fraud (sampling) rate. We tested LR as a linear model and RF as the best nonlinear model and the RE baseline model. For the LR and RF models, we tested two versions of each, one using only trans features and one using both trans and additional aggregated features. Results are given in Table 6.  
Concerning the first question, the results suggest that the newly developed models considerably outperformed the RE model on most metrics. To be fair, the new models are  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Recall** & **Precision** & **F1** & **AP** \\ \hline Baseline (RE) & 0.564 & 0.378 & 0.453 & 0.189 \\ LR.5.C.sm & 0.451 & 0.759 & 0.566 & 0.385 \\ LR.5.C.sm.trans & 0.375 & 0.749 & 0.500 & 0.308 \\ RF.5.C.sm & 0.519 & 0.934 & 0.667 & 0.515 \\ RF.5.C.sm.trans & 0.411 & 0.893 & 0.563 & 0.399 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Classifier performance for the 5.sm.C dataset and different feature sets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classifier** & **Recall** & **Precision** & **F1** & **AP** \\ \hline Baseline (RE) & 0.564 & 0.378 & 0.453 & 0.189 \\ LR.5.A.sm & 0.454 & 0.766 & 0.570 & 0.388 \\ LR.5.B.sm & 0.454 & 0.763 & 0.569 & 0.387 \\ LR.5.C.sm & 0.451 & 0.759 & 0.566 & 0.385 \\ MLP.5.A.sm & 0.454 & 0.786 & 0.576 & 0.393 \\ MLP.5.B.sm & 0.441 & 0.707 & 0.543 & 0.354 \\ MLP.5.C.sm & 0.464 & 0.739 & 0.570 & 0.391 \\ RF.5.A.sm & 0.517 & 0.932 & 0.665 & 0.512 \\ RF.5.B.sm & 0.517 & 0.932 & 0.665 & 0.512 \\ RF.5.C.sm & 0.519 & 0.934 & 0.667 & 0.515 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Classifier performance for 5.sm dataset and different model positions in the pipeline.  
optimized for the evaluation measures that we used in the evaluation, while the RE model is not, and these differences are somewhat expected. As for the second question, in most cases, the RF model was better, which is expected as it is nonlinear.  
To answer the third question, we compared LR and RF variants using the aggregated features with those that do not use them. For both models, adding the aggregated features led to a significant performance improvement.  
Finally, we can conclude that both (1) making the model nonlinear as well as (2) adding aggregated features both help improve performance. In this problem and dataset, the gains from using the nonlinear model were similar to gains from adding the aggregated features: both positively impacted the AP and F1 scores (Table 6). Moreover, using both modifications increased the performance even further, implying that their effects are complementary.