scholarly journals Performance Evaluation of Machine Learning Methods for Credit Card fraud Detection using SMOTE and AdaBoost

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Emmanuel Ileberi ◽  
Sun Yanxia ◽  
Zenghui Wang
Keyword(s):  
Machine Learning ◽  
Performance Evaluation ◽  
Credit Card ◽  
Fraud Detection ◽  
Learning Methods ◽  
Credit Card Fraud ◽  
Machine Learning Methods

Credit Card Fraud Detection - Machine Learning methods

2019 ◽  
Author(s):  
Dejan Varmedja ◽  
Mirjana Karanovic ◽  
Srdjan Sladojevic ◽  
Marko Arsenovic ◽  
Andras Anderla
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Fraud Detection ◽  
Learning Methods ◽  
Credit Card Fraud ◽  
Machine Learning Methods

scholarly journals An Real Time E Commerce application to Find Credit Card Fraud

2021 ◽  
pp. 86-91
Author(s):  
G Yagnadatta ◽  
Nitesh N ◽  
Mohit S ◽  
Padmini M S
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Real Time ◽  
Credit Card ◽  
Detection System ◽  
Learning Algorithms ◽  
Fraud Detection ◽  
Credit Card Fraud ◽  
Machine Learning Methods ◽  
Unethical Activity

Credit card fraud detection is one of the prominent problem in today's world. It is due to the extensive rise in both online and e-commerce transactions. The fraud happens when the users’ accessible card gets stolen from any unauthorized source or the use of credit card for fraudulent purposes. The present scenario is facing this kind of problem. So to detect the unethical activity, the credit card detection system was introduced. The main aim of this research is to focus on machine learning methods. So the algorithms used are unsupervised learning algorithms.

Auto loan fraud detection using dominance-based rough set approach versus machine learning methods

2021 ◽  
Vol 163 ◽  
pp. 113740 ◽  
Author(s):  
Jerzy Błaszczyński ◽  
Adiel T. de Almeida Filho ◽  
Anna Matuszyk ◽  
Marcin Szeląg ◽  
Roman Słowiński
Keyword(s):  
Machine Learning ◽  
Rough Set ◽  
Fraud Detection ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals Credit Card Fraud Detection System

2020 ◽  
Vol 6 (12) ◽  
pp. 24-27
Author(s):  
Shashank Singh and Meenu Garg
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Data Science ◽  
Detection System ◽  
Fraud Detection ◽  
Detection Problem ◽  
Credit Card Fraud ◽  
Local Outlier Factor ◽  
Local Outlier ◽  
Isolation Forest

It is essential that Visa organizations can distinguish false Mastercard exchanges so clients are not charged for things that they didn't buy. Such issues can be handled with Data Science and its significance, alongside Machine Learning, couldn't be more important. This undertaking expects to outline the demonstrating of an informational collection utilizing AI with Credit Card Fraud Detection. The Credit Card Fraud Detection Problem incorporates demonstrating past Visa exchanges with the information of the ones that ended up being extortion. This model is then used to perceive if another exchange is fake. Our target here is to identify 100% of the fake exchanges while limiting the off base misrepresentation arrangements. Charge card Fraud Detection is an average example of arrangement. In this cycle, we have zeroed in on examining and pre- preparing informational indexes just as the sending of numerous irregularity discovery calculations, for example, Local Outlier Factor and Isolation Forest calculation on the PCA changed Credit Card Transaction

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