Adaptive Credit Card Fraud Detection Techniques Based on Feature Selection Method

2019 ◽  
pp. 167-178 ◽  
Author(s):  
Ajeet Singh ◽  
Anurag Jain
Keyword(s):  
Feature Selection ◽  
Credit Card ◽  
Feature Selection Method ◽  
Fraud Detection ◽  
Selection Method ◽  
Credit Card Fraud ◽  
Detection Techniques

SVM-Based Credit Rating and Feature Selection

2014 ◽  
Vol 618 ◽  
pp. 573-577 ◽  
Author(s):  
Yu Qiang Qin ◽  
Yu Dong Qi ◽  
Hui Ying
Keyword(s):  
Logistic Regression ◽  
Feature Selection ◽  
Financial Institutions ◽  
Credit Card ◽  
Credit Rating ◽  
Feature Selection Method ◽  
Selection Method ◽  
Support Vector ◽  
Vector Machines ◽  
Reference Agency

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit rating for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines (SVM) against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default.

Credit Card Fraud Detection Techniques – A Survey

2020 ◽  
Author(s):  
Nikita Shirodkar ◽  
Pratikesh Mandrekar ◽  
Rohit Shet Mandrekar ◽  
Rahul Sakhalkar ◽  
K.M. Chaman Kumar ◽  
...  
Keyword(s):  
Credit Card ◽  
Fraud Detection ◽  
Credit Card Fraud ◽  
Detection Techniques

scholarly journals An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions

2019 ◽  
Vol 14 (6) ◽  
pp. 670-690 ◽  
Author(s):  
Ajeet Singh ◽  
Anurag Jain
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Evaluation Criteria ◽  
Fraud Detection ◽  
Machine Learning Techniques ◽  
Credit Card Fraud ◽  
Detection Techniques ◽  
Digital World ◽  
Learning Techniques ◽  
Sensitivity Specificity

Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets.The performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system.This study has also covered the deep analysis and comparison of the performance (i.e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area.The findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently.This Study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field.

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