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 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 Machine Learning Model for Credit Card Fraud Detection- A Comparative Analysis

2021 ◽  
Author(s):  
Pratyush Sharma ◽  
Souradeep Banerjee ◽  
Devyanshi Tiwari ◽  
Jagdish Chandra Patni
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Credit Card ◽  
Detection System ◽  
Fraud Detection ◽  
Learning Model ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Machine Learning Model ◽  
Artificial Neural Network Ann

In today's world, we are on an express train to a cashless society which has led to a tremendous escalation in the use of credit card transactions. But the flipside of this is that fraudulent activities are on the increase; therefore, implementation of a methodical fraud detection system is indispensable to cardholders as well as the card-issuing banks. In this paper, we are going to use different machine learning algorithms like random forest, logistic regression, Support Vector Machine (SVM), and Neural Networks to train a machine learning model based on the given dataset and create a comparative study on the accuracy and different measures of the models being achieved using each of these algorithms. Using the comparative analysis on the F_1 score, we will be able to predict which algorithm is best suited to serve our purpose for the same. Our study concluded that Artificial Neural Network (ANN) performed best with an F_1 score of 0.91.

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 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.

scholarly journals Financial Fraud Detection Mechanisms to Overcome Trust Issues within Trade Segments

2020 ◽  
Vol 9 (7) ◽  
pp. 663-668
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Execution Time ◽  
Classification Accuracy ◽  
Credit Card ◽  
Fraud Detection ◽  
Support Vector ◽  
Similarity Matrix ◽  
Implementation Tool ◽  
Trading Network

: Frauds in modern era are cause of concern in almost every field of life. Credit card, money laundering and bank frauds are common and technology has to play important part in overcoming this issue. This paper provides insight into financial frauds leading from malicious users in trading network. To this end several techniques are researched over. To start with price based fraud detection is discussed and then similarity matrix, linear binary patterns, support vector machine and random forest in the field of fraud detection are elaborated. This paper highlights pros and cons of each of such techniques. Dataset required determining classification accuracy of these approaches is synthetically driven. Execution time while determining frauds is critical entity and similarity matrix approach is fast and accurate as compared to random forest, support vector and linear binary patterns. Parameters: Classification Accuracy, Execution time Implementation tool: Matlab 2018 Achievement: support vector machine results are closer to similarity matrix based approach in terms of classification accuracy but execution time of similarity based approach is much less and hence this algorithm is considered better in determining financial frauds.

Sign in / Sign up

Export Citation Format

Share Document