# 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
In order to solve the unbalanced dataset problem, the Random Undersampling approach is employed to build a new balanced data frame. It has been observed that Random Undersampling can employ more authentic data and help to solve the unbalanced situation. During implementation, fraud proportions of 1%, 2%, and 5% are taken and randomly selected the same size as legitimate transactions. The CCFDP method's performance is better than that of other current models from the accuracy perspective depicted in Table 4. All of the approaches considered are trained using the same training data and fraud rates. The result demonstrates that the proposed CCFDP detects fraud cases more accurately regardless of the proportion of available fraud rate in the dataset. The CCFDP outperforms all others on diverse sample sets. On the other hand, the proposed CCFDP has the capability to prevent fraud, whereas the contending methods do not have the capability to prevent fraud because they are designed for only fraud detection. The results shown in Table 5 validate the fraud prevention capability of the proposed method. The performance of the proposed CCFDP is also better than competing methods

Figure 12: (**a**) Root Mean Square Error for the proposed CCFDP and contending approaches with maximum 45,000 transactions. (**b**) Relative root mean squared error for the proposed CCFDP and contending approaches with maximum 45,000 transactions. (**c**) Mean Bias Error for the proposed CCFDP and contending approaches with maximum 45,000 transactions. (**d**) Mean directional accuracy for the proposed CCFDP and contending approaches with maximum 45,000 transactions. (CATCHM, LSTM-RNN, CSLMLE, CCFDM, ESDL, ITCCFD, and BTG from the perspective of the statistical performance metrics (MDA, MBE, RRMSE, RMSE). The main reason for acquiring better accuracy is the use of integration of different modern techniques (RU, t-SNE, PCA, LRL, and SVD). These techniques perform a quicker data training process for increasing accuracy. Furthermore, these integrated techniques help to detect fraud successfully. PCA is employed to obtain lower-dimensional data while retaining as much variation in the data. The exploratory data analysis and predictive modeling are performed to reduce dimensionality by projecting each data point onto only the first few major components. In order to further improve accuracy, t-SNE is used to reduce dimensionality by keeping similar and dissimilar instances apart. LRL is also used to assess the success and failure probability of CNP fraud. In order to predict the relationship between various legitimate and illegitimate transactions, the interaction of predictor factors is simulated. However, the integration of modern techniques can increase the complexity. Thus, this issue has been resolved by using the dimensionality reduction feature process. This process helps to describe the procedure for reducing the number of variables in training data. When working with high-dimensional data, it may be useful to reduce dimensionality by projecting the data to a lower-dimensional subspace that captures the core of the data. The challenge encountered is the process of integration of different techniques. ## 6 Conclusions

The rise in fraudulent activity is massively increased due to e-banking transactions that put a burden on fraud control systems. In this research, we present a CCFDP method for detecting credit card fraud detection and prevention. The proposed method involves FDP and FPP. The FDP consists of four cutting-edge methods used in the FDP module: RU, t-SNE, PCA, and SVD processes. The FPP uses logistic regression learning. Furthermore, the Random Under Sampling approach has been employed to increase detection accuracy by balancing the number of fake samples with authentic ones. Different tests have been conducted on 1%, 2%, and 5% fraud proportions to demonstrate the efficiency of the proposed CCFDP. The results confirm that our proposed method has greater fraud detection accuracy.