Credit Card Fraud Detection Using Support Vector Machine

2022 ◽  
pp. 27-37
Author(s):  
Sheo Kumar ◽  
Vinit Kumar Gunjan ◽  
Mohd Dilshad Ansari ◽  
Rashmi Pathak
Keyword(s):  
Support Vector Machine ◽  
Credit Card ◽  
Fraud Detection ◽  
Support Vector ◽  
Credit Card Fraud

scholarly journals Credit Card Fraud Detection Using Weighted Support Vector Machine

2020 ◽  
Vol 11 (12) ◽  
pp. 1275-1291
Author(s):  
Dongfang Zhang ◽  
Basu Bhandari ◽  
Dennis Black
Keyword(s):  
Support Vector Machine ◽  
Credit Card ◽  
Fraud Detection ◽  
Support Vector ◽  
Credit Card Fraud

scholarly journals Application of Credit Card Fraud Detection Based on CS-SVM

2021 ◽  
Vol 11 (1) ◽  
pp. 34-39
Author(s):  
Chenglong Li ◽  
◽  
Ning Ding ◽  
Haoyun Dong ◽  
Yiming Zhai ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Credit Card ◽  
Naive Bayes ◽  
Search Algorithm ◽  
Fraud Detection ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Support Vector ◽  
Credit Card Fraud

With the development of e-commerce, credit card fraud is also increasing. At the same time, the way of credit card fraud is also constantly innovating. Support Vector Machine, Logical Regression, Random Forest, Naive Bayes and other algorithms are often used in credit card fraud identification. However, the current fraud detection technology is not accurate, and may cause significant economic losses to cardholders and banks. This paper will introduce an innovative method to optimize the support vector machine by cuckoo search algorithm to improve its ability of identifying credit card fraud. Cuckoo search algorithm improves classification performance by optimizing the parameters of support vector machine kernel function (C, g). The results demonstrate that CS-SVM is superior to SVM in Accuracy, Precision, Recall, F1-score, AUC, and superior to Logistic. Regression, Random Forest, Decision Tree, Naive Bayes, whose accuracy is 98%.

scholarly journals Support Vector Machine with Information Gain Based Classification for Credit Card Fraud Detection System

2021 ◽  
Vol 18 (2) ◽  
Keyword(s):  
Support Vector Machine ◽  
Credit Card ◽  
Information Gain ◽  
Detection System ◽  
Fraud Detection ◽  
True Positive Rate ◽  
Attribute Selection ◽  
Support Vector ◽  
Large Sample Size ◽  
High True Positive Rate

In the credit card industry, fraud is one of the major issues to handle as sometimes the genuine credit card customers may get misclassified as fraudulent and vice-versa. Several detection systems have been developed but the complexity of these systems along with accuracy and precision limits its usefulness in fraud detection applications. In this paper, a new methodology Support Vector Machine with Information Gain (SVMIG) to improve the accuracy of identifying the fraudulent transactions with high true positive rate for the detection of frauds in credit card is proposed. In SVMIG, the min-max normalization is used to normalize the attributes and the feature set of the attributes are reduced by using information gain based attribute selection. Further, the Apriori algorithm is used to select the frequent attribute set and to reduce the candidate’s itemset size while detecting fraud. The experimental results suggest that the proposed algorithm achieves 94.102% higher accuracy on the standard dataset compared to the existing Bayesian and random forest based approaches for a large sample size in dealing with legal and fraudulent transactions

scholarly journals Credit Card Fraud Detection Framework - A Machine Learning Perspective

2020 ◽  
pp. 431-435
Author(s):  
Jasmin Parmar ◽  
Achyut C. Patel ◽  
Mayur Savsani
Keyword(s):  
Credit Card ◽  
Nearest Neighbor ◽  
Financial Institution ◽  
Confusion Matrix ◽  
Fraud Detection ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Specific System ◽  
Credit Card Fraud ◽  
Credit Score

The short improvement withinside the E-Commerce enterprise has caused a dramatic enlargement withinside the usage of credit score playing cards for on-line buys and thusly they had been flooded with the fraud diagnosed with it. As of late, for banks has gotten extraordinarily tough for figuring out the fraud with inside the credit card framework. Machine getting to know assumes an essential component in distinguishing credit card fraud withinside the transactions. For foreseeing those transactions banks make use of specific system getting to know methodologies, beyond data has been accrued and new highlights are being applied for enhancing the prescient force. The exhibition of possible threats identification in credit card instances is highly prompted through the analysing technique at the informational collection, the dedication of factors, and discovery strategies applied. This paper explores the presentation of K-Nearest Neighbor, Decision Trees, Support Vector Machine (SVM), Logistic Regression, Random Forest, and XGBoost for credit card fraud detection. Dataset of credit card transactions is accrued from Kaggle and it includes a sum of 2,84,808 credit card transactions of an EU financial institution dataset. It depicts doubtful transactions as fraud & labels it "high-quality class" and actual ones as the "poor class". The dataset is relatively imbalanced, it has approximately 0.172% of fraud cases and the relaxations are actual transactions. These methods are implemented for the dataset and work is carried out in Python. The presentation of the methods is classed relying on the accuracy and F1 rating and confusion matrix. Results display that every set of rules may be used for credit card fraud detection with excessive precision. The proposed version may be helpful for the invention of numerous anomalies.

Comparative study on credit card fraud detection based on different support vector machines

2021 ◽  
Vol 25 (1) ◽  
pp. 105-119 ◽  
Author(s):  
Chenglong Li ◽  
Ning Ding ◽  
Yiming Zhai ◽  
Haoyun Dong
Keyword(s):  
Support Vector Machines ◽  
Kernel Function ◽  
Credit Card ◽  
Fraud Detection ◽  
Support Vector ◽  
Model Parameters ◽  
Accuracy Rate ◽  
Function Type ◽  
Credit Card Fraud ◽  
Vector Machines

Credit card fraud is the new financial fraud crime accompanied by the gradual development of the economy which causes billions of dollars of losses every year. Credit card fraud case not only seriously violated the cardholder benefits and financial institutions, but also undermined the credit management order. However, fraudsters keep exploring new crime strategies constantly which exacerbates the crime rate of fraud. Thus, a predictive model for credit card fraud detection is essential to minimize its losses. By distinguishing between fraud and non-fraud, machine learning is one of the most efficient solutions for detecting fraud. Support vector machines have proven to be a novel algorithm with excellent performance. Nevertheless, the performance of SVM depends largely on the correct choice of model parameters (C and g), which could cause that the false positive was very high if the kernel function type and parameter cannot be selected properly. In this paper, based on the real transaction data of the credit card business, firstly, it will find the optimal kernel function suitable for the data set. Secondly, this paper will propose the method of optimizing the support vector machine parameters by the cuckoo search algorithm, genetic algorithm and particle swarm optimization algorithm. Last but not least, the Linear kernel function was found to be the best kernel function with an accuracy rate of 91.56%. Furthermore, the Radial basis function is used to optimize the kernel function, which can improve the accuracy from 42.86% to the highest accuracy rate of 98.05%. Compared with CS-SVM and GA-SVM, PSO-SVM has the best overall performance.

scholarly journals Credit Card Fraud Detection System Using Machine Learning

2021 ◽  
Vol 13 (4) ◽  
pp. 24-37
Author(s):  
Angela Makolo ◽  
◽  
Tayo Adeboye
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Performance Metrics ◽  
Detection System ◽  
Fraud Detection ◽  
Support Vector ◽  
Multivariate Normal ◽  
Data Set ◽  
Credit Card Fraud ◽  
Key Factor

The security of any system is a key factor toward its acceptability by the general public. We propose an intuitive approach to fraud detection in financial institutions using machine learning by designing a Hybrid Credit Card Fraud Detection (HCCFD) system which uses the technique of anomaly detection by applying genetic algorithm and multivariate normal distribution to identify fraudulent transactions on credit cards. An imbalance dataset of credit card transactions was used to the HCCFD and a target variable which indicates whether a transaction is deceitful or otherwise. Using F-score as performance metrics, the model was tested and it gave a prediction accuracy of 93.5%, as against artificial neural network, decision tree and support vector machine, which scored 84.2%, 80.0% and 68.5% respectively, when trained on the same data set. The results obtained showed a significant improvement as compared with the other widely used algorithms.

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