 Open Access
 Authors : Sushant Kumbhar , Ashish Lade , Abhishek Patil , Jaykishan Pandey, A. B. Ghandat
 Paper ID : IJERTV12IS030209
 Volume & Issue : Volume 12, Issue 03 (March 2023)
 Published (First Online): 07042023
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Support Vector Machine based Credit Card Fraud Detection
Sushant Kumbhar
Dept. of Computer Engineering JSCOE Savitribai Phule Pune University Pune, INDIA
Jaykishan Pandey
Dept. of Computer Engineering JSCOE Savitribai Phule Pune University Pune, INDIA
Ashish Lade
Dept. of Computer Engineering JSCOE Savitribai Phule Pune University Pune, INDIA
Prof. A. B. Ghandat
Dept. of Computer Engineering JSCOE Savitribai Phule Pune University Pune, INDIA
Abhishek Patil
Dept. of Computer Engineering JSCOE Savitribai Phule Pune University Pune, INDIA
AbstractCredit card fraud is a major concern in today's world, as it involves the illegal use of credit cards to obtain goods or services. The process of credit card fraud often involves laundering dirty money, making it difficult to trace the source of funds. With the large volume of financial transactions happening globally, It might be difficult to identify credit card theft. In the past, antifraud suites were introduced to detect suspicious activity on individual transactions, but they were not effective in detecting fraud across multiple bank accounts. To overcome this challenge, we propose using machine learning techniques, specifically the Structural Similarity method, to identify common attributes and behavior across multiple bank account transactions. It can be challenging to identify credit card fraud from huge datasets, so we also suggest utilizing case reduction techniques to shrink the input dataset and then looking for pairs of transactions with similar characteristics and behaviours.
Keywords Support Vector Machine, Haar Cascade Algorithm, Fraud Detection

INTRODUCTION
The COVID19 pandemic in 2020 led to a surge in online shopping as travel was restricted and many were confined to their homes. This increase in online shopping also resulted in a significant rise in online financial fraud, particularly involving credit and debit cards. According to a NCRB study, there was a 225% increase in such fraud incidents in 2020 compared to 2019, with a total of 1194 cases reported in 2020 as opposed to 367 in 2019. As a result of the pandemic, many people were introduced to the convenience of internet shopping, but also had to face the unfortunate reality of online fraud.
AIMS & OBJECTIVES
The aim of this project is to classify credit card transactions as either fraudulent or legitimate by using various classification techniques. It offers a userfriendly interface where users can upload a csv file containing transaction and payment information and determine whether the transactions fall into the fraudulent or nonfraudulent category. The core objectives of this project are to identify fraudulent transactions and to provide a solution to the realworld problem. The steps taken to achieve these goals are through the implementation of various classification techniques. The steps followed to manage these goals:

Selection of dataset

Visualization of the features through graphical information

Identification and handling of null values in the dataset

Data preprocessing to eliminate unnecessary parameters

Training using SVM classifiers

Evaluation of the model using test data

Comparison of accuracy, precision, and recall to determine the optimal model

Development of a Graphical User Interface to apply the model to realtime customer data and predict if transactions are legitimate or fraudulent.


LITERATURE SURVEY
The authors of this study evaluate the effectiveness of three machine learning algorithms in detecting credit card fraud by applying them to a set of fraud data. They compare the performance of the Random Forest, Decision Tree, and XGBOOST algorithms, and find that the Random Forest approach shows the highest accuracy in identifying fraud cases [8].
The authors present a technique to assess the daily changes in a facetoface credit card transaction dataset, which pertains to cardholders who make purchases instore. The method involves comparing the transactions of different days by means of a classification process, which evaluates the efficiency of the classification. The more effective the classification, the greater the difference in buying patterns between two days, and vice versa. This leads to the creation of a distance matrix that summarizes the shift in the dataset [6].
The dataset for credit card fraud detection from Kaggle machine learning, which has a highly skewed distribution, was employed in this study. The evaluation focuses on features that are marked either 1 for fraud or 0 for nonfraud cases. Fraud detection is a crucial aspect in the banking industry, and the existing systems suffer from misclassifications and high false positive rates. The paper explores the use of convolutional neural network layers to develop a model for detecting credit
card fraud, with the goal of achieving a high level of accuracy [7].

SYSTEM ARCHITECTURE
Credit card fraud detection is a widely researched subject, with a significant amount of research being conducted. Various statistical methods have been used to develop solutions for detecting credit card fraud are as follows:

Knearest neighbor:
Knearest neighbor (KNN) is a simple and widely used supervised learning technique for classification. The basic idea behind KNN is to divide the data into different clusters by defining K centroids, one for each cluster. However, the placement of these centroids can impact the accuracy of the results, as different locations can result in different outcomes. A crucial aspect of KNN is selecting the appropriate value of K, as a small value increases the likelihood of overfitting while a larger value may lead to increased computation time and underfitting of the model. When presented with new, unseen data, the KNN classifier searches the pattern space for the K closest examples, and uses them to make a prediction.

Logistic Regression:
Logistic Regression is a method for predicting discrete outcomes by using a combination of continuous and discrete predictors. In the case of binary logistic regression, the dependent variable has two levels. The goal of the analysis is to understand the impact of various explanatory variables, which can be either numerical or categorical, on the binary outcome of interest. The objective is to identify the most appropriate model that describes the relationship between the binary outcome and the set of independent variables.

Decision tree:
A decision tree is a visual tool that is used to aid in the process of decision making. It is represented as a diagram that has a flowchartlike structure, in which the nodes represent tests on different attributes, the branches represent the results of those tests, and the leaf nodes represent the final decisions or class labels. Decision trees are closely related to another decisionmaking tool, called influence diagrams, which are also used to calculate the potential outcomes of different options.

PROBLEM STATEMENT
Due to activities like illegal purchases, identity theft, and the use of fake cards, credit card fraud is a severe threat for financial institutions, businesses, and customers everywhere. Credit card fraud detections major goal is to spot and stop fraudulent transactions in realtime while reducing the amount of false positives to prevent interfering with legal transactions. A trustworthy and effective fraud deection system that can examine a large number of transactions, identify odd trends, and rapidly notify pertinent parties of the need for action is needed in order to do this. The ultimate goal is to create a reliable and efficient fraud detection system that will guard against fraudulent activity and safeguard all stakeholders financial interests.
Fig. 1. Architecture Diagram.

ALGORITHMS

Support Vector Machine Algorithm
A popular approach in supervised machine learning called the Support Vector Machine (SVM) is particularly good at handling classification and regression issues. Finding a decision boundary or hyperplane that can successfully divide the data into multiple classes is the basic tenet of SVM. To do this, the algorithm identifies the most critical data points in the dataset, which are called support vectors. These support vectors are the key to creating a hyperplane that separates the data effectively, which is the primary objective of the SVM algorithm. The support vectors, which are the most extreme points in the dataset, are used to determine the decision boundary.
Support Vector Machine (SVM) is a powerful algorithm used in supervised machine learning for solving classification and regression problems. It works especially well when the data cannot be separated into classes using a straight line because this means the data cannot be separated into classes linearly. In these circumstances, SVM employs a method known as the kernel trick, which raises the datas dimension and makes it linearly separable. This enables the algorithm to find a decision boundary or hyperplane that can effectively classify the data.
Regression issues, where the algorithm discovers a function that can forecast a continuous value for a given input, can also be solved using SVM. The hyperplanebased function aims to minimize the difference between the expected and actual values. SVM is resilient to noise and overfitting and includes a regularization parameter that can aid in preventing overfitting. In summary, Support Vector Machine (SVM) is a powerful algorithm that finds a wide range of applications in machine learning, particularly for tasks such as binary classification, text categorization, bioinformatics, and face detection. It is a valuable tool because it can handle nonlinear data, determine the optimum decision boundary by figuring out the support vectors, and is based on the principle of structural risk minimization. An equation representing the SVM decision function, An equation that expresses the SVM decision function and a representation of the SVM decision function are both essential components of the algorithm is Eq. (1)
The Support Vector Machine (SVM) algorithm uses an
equation, known as Eq. (1), to determine the boundary that separates two classes. This equation includes an input vector, x, which comprises of weight and a constant term, b. The algorithm learns the values of the weight, w, and the constant, b, during the training phase, to classify new instances. The goal of the SVM approach is to find the ideal values of w and b in order to maximise the separation between the two classes. The algorithms ultimate goal is to increase the margin between the two classes.
The SVM algorithm uses a metric called the Margin, which is the distance between the two hyperplanes, to determine the optimal hyperplane that separates the two classes. The larger the margin, the better the separation of the two classes. The SVM algorithm aims to maximize this margin to find the best
decision boundary by = . + = 0 and two hyper planes are 1 = . + = +1 & 2 = . +
= 1.The stands for the threshold that divides the two classes, and 1 and 2 represent the margins that separate the two classes. 2/ denotes the margin. The vectors ws norm is represented as . When the data is not perfectly
separable, meaning there are instances of overlap between the
two classes, the margin becomes soft. This increases the likelihood of misclassification errors. To reduce these errors, the SVM algorithm aims to maximize the margin. By maximizing the margin, the algorithm tries to find the best decision boundary that separates the two classes with the least
amount of errors. The slack variable = 0 is used to
minimize the errors. We can show correctly classified classes
using = 0. Let assume the nonnegative slack variable for misclassification is .
In the instance of fraud detection, y = 1 for the positive
class, therefore let y be the indication of the class and y = 1 is
a class that represents a negative class. Either . + >= 1 or . + >= 1 + , which can be summed
up by Eq, is necessary for SVM (2),
where, = 1,2, . . . , n
Eq. (3) in SVM defines the optimization problem for figuring
out the values of w and b.
SVM reduces complexity by minimizing 2/2 and minimizes misclassification errors by minimizing the slack
variable. The regularization parameter C is used to balance the tradeoff between the two classes and weighs the classification errors. Utilizing the Lagrange function 4, the constrained optimization problem is resolved [10].
By minimising w, b & and maximising & , the optimization problem is solved. The dual formulation in Eq. should be introduced to the problem in order to better resolve it (5),
This substitution transforms the issue into its twin formulation, which is defined by,
and is optimised within the restrictions,
Eq. (7) is subjected to the KuhnTucker condition in Eq (6), where, = 1, 2, . . ., n.
The Lagrange vectors, sometimes referred to as support
vectors, are used to characterise the hyperplane. When the data can be separated linearly, all support vectors are on the edge. Eq. (8) in the SVM algorithm determines the decision threshold.
The SVM algorithm uses an equation, This comprises the bias term b, the inner product of (x, xi), the number of support vectors NS, and the input vector x. When the data is nonlinear, meaning it cannot be separated by a straight line, a technique known as the kernel trick is applied to transform the input vector into a higher dimensional feature space. This enables the algorithm to find a decision boundary, also known as a hyperplane, that can effectively classify the data. Utilizing kernel functions, which offer a technique to extend linear algorithms to nonlinear data by expressing them in terms of dot products, allows for the implementation of the kernel trick. Any function that meets Vapniks definition of Mercers condition can be employed as a kernel function [11]. The equation, which is a formal description of a kernel function in the SVM method,
The kernel function can transform the input vectors into a higher dimensional feature space.

Haar Cascade Algorithm
Identification of instances of things in photos and videos is the focus of the field of object detection, which combines computer vision, image processing, and deep learning. In this article, we will use a technique called Haar cascades for object detection. Paul Viola and Michael Jones created, Haar Cascade classifiers are a popular method for object detection. They are machine learningbased and trained using a large number of positive and negative images. Negative photos are of everything else that does not include the target object, whereas positive images are of the objects we want to detect.
Haar cascade is an algorithm that is capable of detecting objects in images, regardless of their size and location. It is a relatively simple algorithm that can operate in realtime. Haar cascade uses a cascading window approach, where it analyses the features in each window to determine whether an object could be present. The approach analyses windowsized sections of the picture to compute and match features using sample Haar features. It performs the role of a classifier by clasifying data points as positive if they are a part of the identified item and negative if they are not.
Haar cascades are efficient and can perform well in real time applications, however, they are not as precise as more recent object detection methods. They also have a downside of predicting many false positives. They are simple to implement and require less computing power. Using a collection of both positive and negative photos, the algorithm is trained. Positive photos are those we wish to detect, and negative images are those without the thing were trying to find. The algorithm uses the cascading window approach, where it computes features in each window and classifies whether it could be an object. The Haar cascade features are used to traverse windowsized areas of the image to compute and match features. The algorithm uses the concept of weak classifiers and strong classifiers to detect the object. The weak classifier is a simple classifier that is not very accurate but when combined with many other weak classifiers it will form a strong classifier that is very accurate.
The Haar cascade algorithm is commonly used in realtime applications such as face detection, object tracking, and video surveillance. It is also applied in driver assistance systems in vehicles to detect pedestrians and other objects on the road. Despite its widespread usage, the Haar cascade algorithm has some limitations. It is not as precise as other contemporary object detection techniques and it is sensitive to changes in the lighting conditions of the image.


METHODOLOGY

Methodology Description
A machine learning model for detecting credit card fraud must be built using a few crucial steps:

Uploading the dataset: To start creating the model, the dataset comprising data on credit card transactions must be uploaded to the system.

Data preprocessing and selection: At this point, the datasets features are transformed into numerical data and the relevant attributes that best represent the behaviour of a particular credit card account are selected. It is important to only include features that
are relevant to the task at hand, as including irrelevant features can make the classifier less efficient.

Feature extraction: In this step, the input data are condensed into a smaller set of features that represent the key aspects of the data. For this, principal component analysis (PCA) is a helpful tool.

SVM training and classification: A popular supervised machine learning approach for classification tasks is the Support Vector Machine (SVM). The SVM algorithm seeks to identify the best hyperplane in an Ndimensional space for classifying the data into distinct groups.

Evaluation: Evaluation of the models performance is the last phase. While accuracy is essential, when talking about credit card fraud detection, it is advised to use the fraud catching rate and false alarm rate. An effective method for assessing these indicators is the confusion matrix.



METHODOLOGY


As stated in the reference [9], while accuracy is important in the fraud detection field, the metrics that matter most are the fraud catching rate and false alarm rate. The confusion matrix is used in this study to assess these rates. The confusion matrix is presented here in its conventional format.
Fig. 2. Confusion Matrix
The anticipated and actual classes are denoted in the column and row, respectively, of the confusion matrix used in credit card fraud detection. The amount of legitimate transactions that were successfully classified as nonfraudulent transactions is shown by the True Positive (TP) value, which stands for the fraud catching rate. The false alarm rate, or false positive (FP) figure, shows the number of legitimate transactions that were mistakenly labelled as fraudulent. The number of fraudulent transactions that were mistakenly thought to be real is shown by the False Negative (FN) value. The number of fraudulent transactions that were accurately classified as such is shown in the True Negative (TN) figure.
Fig. 3. Confusion Matrix
Fig. 4. Correlation of the features
VIII. CONCLUSION
In this paper, a novel strategy for combating credit card fraud is provided. It makes use of a behaviorbased categorization algorithm with the assistance of Support Vector Machines (SVM). A huge number of financial transactions can be analysed by the proposed machine learning framework to look for possible money laundering activities. In order to increase the effectiveness of the framework, a number of case reduction techniques are used to cut down on the number of probable money laundering accounts that need to be examined, incorporating balance score filtering and matching transaction detection. The framework combines accounts that are more likely to be utilised in credit card fraud using the structural similarity method, facilitating the easy detection of patterns and trends that more traditional organising methods tend to conceal. The suggested framework offers a high degree of accuracy in detecting money laundering activities, according to the preliminary experimental results, making it a promising tool for preventing credit card fraud.
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