An Application of Support Vector Machines for Customer Churn Analysis: Credit Card Case

2005 ◽  
pp. 636-647 ◽  
Author(s):  
Sun Kim ◽  
Kyung-shik Shin ◽  
Kyungdo Park
Keyword(s):  
Support Vector Machines ◽  
Credit Card ◽  
Support Vector ◽  
Customer Churn ◽  
Churn Analysis ◽  
Vector Machines

Fuzzy Asymmetric Support Vector Machines

2012 ◽  
Vol 433-440 ◽  
pp. 7479-7486
Author(s):  
Rui Kong ◽  
Qiong Wang ◽  
Gu Yu Hu ◽  
Zhi Song Pan
Keyword(s):  
Support Vector Machines ◽  
Medical Diagnosis ◽  
Credit Card ◽  
Training Data ◽  
Support Vector ◽  
Imbalanced Datasets ◽  
Credit Card Fraud ◽  
Vector Machines ◽  
Imbalanced Training Data ◽  
Error Costs

Support Vector Machines (SVM) has been extensively studied and has shown remarkable success in many applications. However the success of SVM is very limited when it is applied to the problem of learning from imbalanced datasets in which negative instances heavily outnumber the positive instances (e.g. in medical diagnosis and detecting credit card fraud). In this paper, we propose the fuzzy asymmetric algorithm to augment SVMs to deal with imbalanced training-data problems, called FASVM, which is based on fuzzy memberships, combined with different error costs (DEC) algorithm. We compare the performance of our algorithm against these two algorithms, along with different error costs and regular SVM and show that our algorithm outperforms all of them.

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.

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