# Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms
## 2 Related Work
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Works** & **Algorithms for Credit Card Fraud** & **Features/Strengths** & **Deficiencies/Vulnerabilities** \\ \hline \multirow{2}{*}{[PERSON] et al. [16]} & Unsupervised credit card & Improves the model accuracy for credit card fraud detection. & Increases the execution time and there is possibility of the risk \\ \hline \multirow{2}{*}{[PERSON], and [PERSON] [17]} & Evaluation of the multiple algorithms for credit card fraud detection & Determining the best fraud detection algorithm that can help to protect the financial assets & Failed to evaluate the analytical algorithms correctness and effectiveness. The work lacks any uniqueness. \\ \hline \multirow{2}{*}{[PERSON] et al. [18]} & Suggested a paradigm for fraud detection based on CNN & Provides innate fraud behavior features. Additionally, trade entropy is employed for the transaction categorization accuracy & Limited to fraud feature detection but failed to identify complete fraud transactions \\ \hline \multirow{2}{*}{[PERSON] et al. [19]} & \multirow{2}{*}{CNN has been employed after the trade entropy and feature matrices are combined.} & proposes the use of entropy to identify more sophisticated extortion schemes. In addition, many transactions use a characteristic matrix, on which CNN is trained to detect the collection of latent patterns for each sample. \\ \hline \multirow{2}{*}{[PERSON] et al. [20]} & Addressed the issue of false sense of security using use a deep-learning system & The algorithm is highly accurate at spotting new patterns and illegitimate certifications. Additionally, it reduces the time needed to identify new attacks from risky websites \\ \hline \multirow{2}{*}{[PERSON] et al. [21]} & \multirow{2}{*}{A case study involving credit card fraud detection} & To standardize data, cluster analysis is employed. The results of employing cluster analysis and artificial neural networks for fraud detection have shown that clustering properties can reduce neural inputs. \\ \hline \multirow{2}{*}{[PERSON] et al. [22]} & Suggested a fundamentally new model-based strategy that separates anomalies & Since Forest uses sub-sampling, which was previously impossible, the algorithm has a linear time complexity with minimal constant and elementary usage & It is not specially designed for credit card fraud detection. Limited to determining sub-sampling \\ \hline \multirow{2}{*}{[PERSON] et al. [23]} & Method for authenticating a credit card & Provides the procedure for transaction validation before a credit card transaction may be approved & Failed to provide better accuracy for the credit card fraud detection \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] [24]} & Introduces computational intelligence-based solution for monetary fraud detection procedures & The categorization techniques include critical components such as detection rules for detecting various forms of fraud. A higher success rate is obtained & Numerous aspects of intelligent fraud detection have yet not been thoroughly studied \\ \hline \multirow{2}{*}{[PERSON] et al. [25]} & \multirow{2}{*}{Paradigm is used to detect the anomalies} & A new matrix profile is introduced for anomaly detection. The paradigm is applied to huge multivariate data sets and delivers high-quality approximation solutions in a reasonable amount of time with excellent accuracy \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] [26]} & \multirow{2}{*}{Undertaken comparative research on data mining strategies for credit card fraud detection} & Investigating the performance of Random Forest and Support Vector Machines in conjunction with logistic regression to address the issue of an unbalanced dataset. Furthermore, it is proved that the Random Forest method outperforms SVM in terms of accuracy & It is limited to comparison of two algorithms. No novelty and originality \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The current contributions for addressing credit card fraud detection.