# 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
Furthermore, the accuracy of the proposed CCFDP is compared with the state-of-the-art (CATCHM, LSTM-RNN, CSLMLE, CCFDM, ESDL, ITCCFD, and BTG). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Accuracy with 1\%** & **Accuracy with 2\%** & **Accuracy with 5\%** \\  & **Fraud Proposition** & **Fraud Proposition** & **Fraud Proposition** \\ \hline CATCHM [27] & 99.72\% & 99.19\% & 97.97\% \\ \hline LSTM-RNN [28] & 98.95\% & 98.81\% & 97\% \\ \hline CSLMLE [29] & 99.84\% & 99.51\% & 98\% \\ \hline CCFDM [30] & 99.32\% & 98.91\% & 97.88\% \\ \hline Proposed CCFDP & 99.94\% & 99.89\% & 99.72\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The fraud detection accuracy of the proposed CCFDP and contending methods. \begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Methods**} & **Fraud Prevention Accuracy \%** \\ \hline CCFDP with 1\% Fraud Proportion & 99.99\% \\ \hline CCFDP with 2\% Fraud Proportion & 99.98\% \\ \hline CCFDP with 3\% Fraud Proportion & 99.95\% \\ \hline CCFDP with 4\% Fraud Proportion & 99.94\% \\ \hline CCFDP with 5\% Fraud Proportion & 99.90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The fraud prevention accuracy of the proposed CCFDP. Based on the comparison result, the proposed CCFDP outperforms current state-of-the-art methods. Moreover, the proposed CCFDP has the capability to prevent fraud as compared to counterpart methods. The result of fraud prevention accuracy also confirms the suitability of the proposed method. In the future, we will assess the proposed CCFDP's time and space complexity, as well as other quality-of-service factors. A.R., conceptualization, writing, idea proposal, software development, methodology, review, manuscript preparation, visualization, results and submission; M.B.H.F., data curation, software development, and preparation; M.A. ([PERSON]), F.A. and N.Z.J., review, manuscript preparation, and visualization; G.B., A.A., M.A. ([PERSON]) and S.A., review, data curation and editing. All authors have read and agreed to the published version of the manuscript. This work was supported by Taif University Researchers Supporting Project number (TURSP-2020/302), Taif University, Taif, Saudi Arabia. The data that support the findings of this research are publicly available as indicated in the reference. Additionally, the links are given as below: [[https://datahub.io/machine-learning/creditcard#data](https://datahub.io/machine-learning/creditcard#data)]([https://datahub.io/machine-learning/creditcard#data](https://datahub.io/machine-learning/creditcard#data)); [[https://github.com/nsethi31/Kaggle-Data-Credit-Card-Fraud-Detection](https://github.com/nsethi31/Kaggle-Data-Credit-Card-Fraud-Detection)]([https://github.com/nsethi31/Kaggle-Data-Credit-Card-Fraud-Detection](https://github.com/nsethi31/Kaggle-Data-Credit-Card-Fraud-Detection)); [[https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)]([https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud)). The authors gratefully acknowledge the support of [PERSON] for providing insights. The authors declare no conflict of interest. ## References

* _[PERSON] et al. (2021)_ [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Blockchain-Enabled Transaction Scanning Method for Money Laundering Detection. _Electronics_**2021**, _10_, 1766. [CrossRef]
* _[PERSON] et al. (2021)_ [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Avoidance of Cybersecurity Threats with the Deployment of a Web-Based Blockchain-Enabled Cybersecurity Awareness System. _Appl. Sci._**2021**, _11_, 7880. [CrossRef]
* _[PERSON] et al. (2022)_ [PERSON]; [PERSON]; [PERSON]; [PERSON] Instance-dependent cost-sensitive learning for detecting transfer fraud. _Eur. J. Oper. Res._**2022**, _297_, 291-300. [CrossRef]
* _[PERSON] et al.