# 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
However, if the user inputs the incorrect code, the system stalls and aborts the transaction, as well as publishing to the dataset. **Theorem 1**. : _Fraudulent transaction initiating rate \(\sigma\), as well as fraud attempt capability should not be more than \(c-k\), the probability \(P_{rd}\) of fraud detection using our proposed CCFDP method can be specified as:_

\[Z=S\sum T^{*} \tag{13}\]8

**Proof. ** Suppose the fraud attempting capability \(c\sigma\) is smaller than \(c-k\), several features of the proposed OCCFP \(c(1-\sigma)\) is larger than \(k\). A suitable detection process is obtained using \(k\) acquired from the number of \(c(1-\sigma)\) legitimate transactions. Thus, the detection process is specified through \(D_{c(1-\sigma)}^{k}\). Thus, the cumulative number of possible detection processes is \(D_{c}\), it ensures that the likelihood of accurate fraud detection can be measured as:

\[Z=S\sum T^{*} \tag{13}\]9

\(\square\)

**4. Implementation and Experimental Results**

This section covers the implementation and results. ### Implementation

The materials utilized during implementation are shown in Figure 3:0 The platform was developed using Ubuntu 18.04 with the Visual Studio IDE Python 3.4.6. We rent clusters with 0.1-s uptime, 16 GB RAM, 256 GB ROM, and Intel Core i7 processors running at 2.4 GHz. MySQL 8.0.31 is the database management system of choice. Figure 4 illustrates a typical case of the credit card fraud. It can be seen that the thief who steals the credit card tries to use the card credentials in order to make electronic purchases and pay it through Internet. Every purchase is known as a one transaction. The payments may be quite small, this is one of the features of the fraudulent behavior. As the thief does not know how much money he can utilize. Next, our proposed CCFDP model will analyze every transaction made by this card, compares with the previous owner behavior. In addition, when the model detects the fraudulent activity, the system sends to the owner's phone a verification code. If the thief fails to verify the code, the transaction will be aborted and then the system tries to verify the cardholder by his secret question, and if at that time the answer will be wrong, the credit card will be blocked, until the card owner unlocks it. Furthermore, the system sends alert message to the administrator. ### Dataset

In order to train our model we have found the dataset from the Kaggle \"creditcard.csv\". This file consists of 31 features. The dataset includes credit card transactions from September 2013. The dataset is unbalanced, with frauds making up 0.172% of all transactions in the positive class. It only has numeric input variables that have undergone Prompt Corrective Action (PCA) transformation. Unfortunately, we are unable to provide the original features and additional context for the data due to confidentiality concerns. The principal components obtained with PCA are the features \(F=\{F_{1},F_{2},\ \ldots,F_{31}\}\). The time and amount are the only features that have not been changed by PCA. The seconds that passed between each transaction and the dataset's first transaction are listed in the feature \"Time.\" The transaction amount is represented by the feature \"Amount,\" which can be used for example-dependent, cost-sensitive learning. The response variable, feature \"Class,\" accepts a value of 1 in cases of fraud and 0 in all other circumstances. We suggest measuring accuracy using the Area Under the Precision-Recall Curve given the class imbalance ratio. For classification that is not balanced, confusion matrix accuracy is meaningless. Based on this dataset, an interesting result have been obtained. Python Plotly library is used to draw the graphical results.