# Credit Card-Not-Present Fraud Detection and Prevention Using Big Data Analytics Algorithms
## 2 Related Work
The paper provided a case study in which Multilayer Perceptron Artificial Neural Networks and Cluster Analysis were used to detect credit card theft. Cluster Analysis was used to successfully perform qualitative data standardization. Ref. [22] suggested a fundamentally new model-based strategy that separates anomalies rather than profiling typical spots. Due to the isolation, iForest employs sub-sampling, which is not possible in previous approaches, resulting in an algorithm with a linear time complication and a low constant and memory use. It is also appropriate for high-dimensional issues with a significant number of irrelevant qualities, as well as when the training set contains no anomalies. There are two phases to detecting anomalies using iForest. The first phase is training, which involves creating isolation trees from subsamples of the training set. The second phase is testing, which involves running the test instances through isolation trees to acquire an anomaly score for each one. In [23], a method of authenticating a credit card is presented that involves the stages of issuing a credit card to a holder, the card bearing an account number. A personal identification number (PIN) is assigned to the holder. A validation transaction must be performed before a credit card transaction may be authorized, in which the customer slips a credit card into an electronic terminal and then enters the PIN. The PIN code and account number are compared to information recorded in a database, and only if the PIN code matches the account number accurately is credit card usage approved. This research [24] provided a complete examination of monetary fraud detection procedures utilizing such data processing methodologies, with a specific focus on computational intelligence-based solutions. Key elements such as detection rule employed, fraud kind explored, and the success rate is covered in the classification of procedures. Problems associated with current techniques, as well as prospective future directions of investigation, have also been identified. The current methods in monetary fraud detection using intelligent techniques, each applicable math, and process, were studied in this investigation [10]. Their effectiveness varied, but each approach was found to be somewhat capable of police work involving various types of monetary fraud. The ability of CI tactics such as neural networks and support vector machines to be informed and adapt to new things is helpful in fighting fraudsters' developing methods. There are still various facets of intelligent fraud detection that have not yet been investigated. Various types of fraud, as well as some data processing tactics, are briefly examined but require further investigation to be fully understood. There is also the possibility to examine the performance of current strategies through customization or standardization, as well as the possibility to assess price profit analysis of method fraud detection. A new matrix profile (NMP) for the anomaly detection is used to tackle the all-pairs similarity search issue for time series data [25]. The proposed paradigm is based on two cutting-edge algorithms: Scalable time-series Ordered-search Matrix Profile (STOP) and scalable time series anytime matrix profile (STAMP). The proposed NMP may be applied to huge multivariate data sets and delivers high-quality approximation solutions in a reasonable amount of time. The findings show that the suggested NMP outperforms the other algorithms in terms of accuracy. Comparative research on data mining strategies for credit card fraud detection is undertaken in this work [26]. The following algorithms were investigated: random forest and SVM were used in conjunction with Logistic Regression. A novel network-based model called CATCHM is introduced for credit card fraud detection [27]. The proposed model is based on representation learning. An inventive network design employs an effective inductive pooling operator and careful configuration of the downstream classifier.