# Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms
## 1 Introduction  
Nowadays, e-commerce is a regular and necessary element of everyday life. It enables instant payment for services and commodities. Nonetheless, for the vast majority of people, the process of transmitting money by air is a \"black box.\" This condition invites scammers who seek to benefit unlawfully [1, 2]. Since fraudulent strategies evolve at a quick pace, it is vital to create an adaptive detection system to retain its effectiveness [3, 4]. Ref. [5] proposed a paradigm for integrating supervised and unsupervised algorithms for detecting credit card fraud. The model enables the discovery of new fraudulent activities and provides a comprehensive picture of the relationships between various variables. Themodel's goal was to increase deduction accuracy. This paradigm, however, falls short in terms of local and global approaches. The integration of supervised and unsupervised data does not give the fine-grained resolution required to reap the benefits of unsupervised data. Ref. [6] compares the performance of four data mining-based fraud detection algorithms (support vector machine, K-nearest neighbors, decision trees, and naive bayes). The models made use of a real-world anonymized data collection of transactions. The performance evaluation was based on four criteria: The true positive rate (TPR), the false positive rate (FPR), the balanced classification rate (BCR), and the Matthews Correlation Coefficient (MCC). The authors found that no data mining approach is universally superior to others, and that progress can only be accomplished by combining several techniques. Ref. [7] demonstrates a novel (APATE) approach for identifying fraudulent credit card transactions in internet stores. It has intrinsic features derived from spending history and incoming transactions using RFM (Recency-Frequency-Monetary) fundamentals, as well as network-based features derived from merchants' and credit card holders' networks, and then deriving a time-dependent suspiciousness score for each network object. According to the findings, intrinsic and network-based features are two sides of the same coin. Combining these two types of features results in models with AUC scores better than 0.98. Ref. [8] demonstrates a CNN-based fraud detection algorithm for identifying the intrinsic patterns of fraud activity gleaned from labeled data. Common approaches to identify fraud features include neglecting different scenarios and having an unbalanced imbalance of positive and negative samples when using rule-based expert systems. Real-world large commercial bank transactions were employed. As a result, when compared to other recent techniques, the performance was outstanding [9]. Facial expression recognition is proposed to track the applicant's expressions throughout the interview [10]. Virtual interviews could significantly reduce the amount of effort required of human resource employees. Ref. [11] analyses the detection accuracy and detection time of a neural network trained on examples of fraud due to lost cards, stolen cards, application fraud, counterfeit fraud, mail-order fraud, and NRI (non-received issue) fraud. When data normalization in artificial neural networks is employed, it has been demonstrated how the initial inquiry assists to minimize neural inputs by grouping attributes [12]. It describes a strategy for decreasing false positives in bank anti-fraud systems that uses rule induction in distributed tree-based models to explicitly differentiate abnormalities rather than profiling usual areas [13]. It makes extensive use of sub-sampling, resulting in an algorithm with linear time complexity, a low constant, and low memory demand. Ref. [14] shows how artificial immune systems (AIS) may be used to detect credit card fraud. It was pitted against neural networks (NN), Bayesian networks (BN), Naive Bayes (NB), and decision trees (DT). It has been determined that AIS performs best with GA-optimized settings.