scholarly journals Predicting Credit Card Transaction Fraud Using Machine Learning Algorithms

2019 ◽  
Vol 11 (03) ◽  
pp. 33-63 ◽  
Author(s):  
Jiaxin Gao ◽  
Zirui Zhou ◽  
Jiangshan Ai ◽  
Bingxin Xia ◽  
Stephen Coggeshall
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals DETECTING CREDIT CARD FRAUD USING MACHINE LEARNING ALGORITHMS

InterConf ◽  
2021 ◽  
pp. 393-403
Author(s):  
Olexander Shmatko ◽  
Volodimir Fedorchenko ◽  
Dmytro Prochukhan
Keyword(s):  
Machine Learning ◽  
Financial Services ◽  
Credit Card ◽  
Banking Sector ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Credit Cards ◽  
Internet Banking ◽  
Machine Learning Algorithms ◽  
Credit Card Fraud

Today the banking sector offers its clients many different financial services such as ATM cards, Internet banking, Debit card, and Credit card, which allows attracting a large number of new customers. This article proposes an information system for detecting credit card fraud using a machine learning algorithm. Usually, credit cards are used by the customer around the clock, so the bank's server can track all transactions using machine learning algorithms. It must find or predict fraud detection. The dataset contains characteristics for each transaction and fraudulent transactions need to be classified and detected. For these purposes, the work proposes the use of the Random Forest algorithm.

scholarly journals Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection

2019 ◽  
pp. 394-401
Author(s):  
Karthik R ◽  
Navinkumar R ◽  
Rammkumar U ◽  
Mothilal K. C.
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Credit Card ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Transaction Data ◽  
Online Transactions ◽  
Spending Behavior ◽  
Financial Transactions

Cashless transactions such as online transactions, credit card transactions, and mobile wallet are becoming more popular in financial transactions nowadays. With increased number of such cashless transaction, number of fraudulent transactions is also increasing. Fraud can be distinguished by analyzing spending behavior of customers (users) from previous transaction data. Credit card fraud has highly imbalanced publicly available datasets. In this paper, we apply many supervised machine learning algorithms to detect credit card fraudulent transactions using a real-world dataset. Furthermore, we employ these algorithms to implement a super classifier using ensemble learning methods. We identify the most important variables that may lead to higher accuracy in credit card fraudulent transaction detection. Additionally, we compare and discuss the performance of various supervised machine learning algorithms that exist in literature against the super classifier that we implemented in this paper.

scholarly journals Detection and Analysis of Credit Card Application Fraud Using Machine Learning Algorithms

2020 ◽  
Vol 1693 ◽  
pp. 012064
Author(s):  
Yaodong Han ◽  
Shun Yao ◽  
Tie Wen ◽  
Zhenyu Tian ◽  
Changyu Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Learning Algorithms ◽  
Machine Learning Algorithms
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