Detection of Credit Card Fraud Transactions Using Machine Learning Algorithms and Neural Networks: A Comparative Study

2018 ◽  
Author(s):  
Deepti Dighe ◽  
Sneha Patil ◽  
Shrikant Kokate
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Comparative Study ◽  
Credit Card ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Credit Card Fraud

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 Comparison of Different Ensemble Methods in Credit Card Default Prediction

2021 ◽  
Vol 5 (2) ◽  
pp. 20-25
Author(s):  
Azhi Abdalmohammed Faraj ◽  
Didam Ahmed Mahmud ◽  
Bilal Najmaddin Rashid
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Credit Card ◽  
Characteristic Curve ◽  
Learning Algorithms ◽  
Ensemble Methods ◽  
Research Problem ◽  
Machine Learning Algorithms ◽  
Default Prediction ◽  
Credit Card Default

Credit card defaults pause a business-critical threat in banking systems thus prompt detection of defaulters is a crucial and challenging research problem. Machine learning algorithms must deal with a heavily skewed dataset since the ratio of defaulters to non-defaulters is very small. The purpose of this research is to apply different ensemble methods and compare their performance in detecting the probability of defaults customer’s credit card default payments in Taiwan from the UCI Machine learning repository. This is done on both the original skewed dataset and then on balanced dataset several studies have showed the superiority of neural networks as compared to traditional machine learning algorithms, the results of our study show that ensemble methods consistently outperform Neural Networks and other machine learning algorithms in terms of F1 score and area under receiver operating characteristic curve regardless of balancing the dataset or ignoring the imbalance

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