# Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms
## 3 Credit Card-Not-Present Fraud Detection and Prevention Method
### Fraud Detection Process
#### 3.1.1 Dimensionality Feature Reduction
The main goal of this experiment is to remove unnecessary features that aid in the reduction of complexity. Furthermore, the reduction in dimensionality results in less storage space and thus less computation time. Reduced misleading data can also improve model accuracy. #### 4.3.6 Validation Rate

Figure 10a-d show the training and validation curves for the most advanced prediction classifiers. According to the results, the Logistic Regression produces the best results because the difference between cross-validation and training score is the smallest. As a result, we have decided to build our prediction model using this algorithm. The Logistic Regression produces a cross-validation rate of 93.35% and a training score of 94.31%. Other algorithms, on the other hand, produce lower results, such as the k-nears neighbors learning algorithm, which produces 92.84% cross-validation and 94.26% training score, and the support vector learning algorithm, which produces 93.76% cross-validation and 96.92% training score, and the decision tree classifier algorithm, which produces 91.98% cross-validation and 94.82% training score. According to the results, Logistic Regression has a smaller difference between cross-validation and training-score, which is found to be 0.96%, whereas other competing algorithms have larger differences. It has been observed that the support vector learning algorithm has a larger difference between cross-validation and training-score, which is 3.16%. #### 4.3.7 Accuracy

In the previous results, the feature reduction dimensionality of the dataset including balanced and imbalanced correlation is performed to determine the accuracy of the proposed CCFDP method. The suitability of the integrated algorithms/models in the proposed method confirmed how to detect normal and fraudulent transactions. The suitability of the proposed CCFDP method is assessed and compared with current state-of-the-art approaches (CATCHM [26], LSTM-RNN [27], CSLMLE [28] and CCFDM [29]). The proposed CCFDP method inherits amazing features of different models (RU, t-SNE, PCA, SVD, and LRL). The integration of these models helps acquire higher accuracy as compared to other competing approaches. Based on the results, it is confirmed that the proposed CCFDP obtains better accuracy. Figure 11a depicts the results based on a 1% fraud proportion. It has been observed that the proposed CCFDP obtained 99.92% fraud detection accuracy, whereas the contending methods obtained the fraud detection accuracy from

Figure 9: (**a**) shows the dimensionality feature reduction using t-SNE, while (**b**) shows the dimensionality feature reduction using PCA and (**c**) shows the dimensionality feature reduction using SVD. 89.95% to 99.82%. In Figure 11b fraud proportion rate has been increased up to 2%. The results demonstrate that the fraud-increase proportion affects the proposed and contending methods. However, the proposed CCFDP is not highly affected. On the other hand, the contending methods are more affected. The CCFDP acquires 99.89% fraud detection rate. The competing methods achieve fraud detection accuracy ranging from 98.81% to 99.51%. Figure 11c depicts the outcome based on a 5% fraud proportion that has been significantly increased. This increase has only a minor impact on the proposed CCFDP, which achieves 99.72% fraud detection accuracy, which is still acceptable in practice. On the other hand, the competing methods significantly reduce fraud detection accuracy, which has been observed to range from 97% to 98%. During the experiments, it was discovered that an increase in fraud proportions has a similar effect on the competing methods. Figure 11d depicts the proposed CCFDP's fraud prevention accuracy at various fraud proportion rates ranging from 1-5%. The fraud prevention accuracy has been significantly improved and is acceptable in real life. Moreover, the increase in fraud proportions does not have a greater negative effect.