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 Credit Card Fraudulent Detection Using Machine Learning Algorithm

2020 ◽  
pp. 445-449
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
High Efficiency ◽  
Performance Metrics ◽  
Learning Algorithm ◽  
Optimal Algorithm ◽  
Credit Cards ◽  
Training Model ◽  
Machine Learning Algorithms ◽  
Machine Learning Models

The fraudulent transactions that occur in credit cards end in huge financial crisis. Since the web transactions has grown rapidly, the results of digitalized process hold an enormous sharing of such transactions. So, the financial institutions including banks offers much value to the applications of fraud detection. The Fraudulent transactions can occur in different ways and in various categories. Our work mainly focuses on detecting the illegal transactions effectively. Those transactions are addressed by employing some machine learning models and therefore the efficient method is chosen through an evaluation using some performance metrics. This work also helps to select an optimal algorithm with reference to the machine learning algorithms. We illustrate the evaluation with suitable performance measures. We use those performance metrics to evaluate the algorithm chosen. Within the existing system the algorithms provide less efficiency and makes the training model slow. Hence within the proposed system we used Multilayer Perceptron and Random Forest to supply high efficiency. From these algorithms efficient one is chosen through evaluation.

scholarly journals Credit Card Fraud Detection System

2021 ◽  
Vol 10 (2) ◽  
pp. 158-162
Author(s):  
Kartik Madkaikar ◽  
◽  
Manthan Nagvekar ◽  
Preity Parab ◽  
Riya Raika ◽  
...  
Keyword(s):  
Machine Learning ◽  
Financial Institutions ◽  
Credit Card ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Paper Machine ◽  
Credit Card Fraud

Credit card fraud is a serious criminal offense. It costs individuals and financial institutions billions of dollars annually. According to the reports of the Federal Trade Commission (FTC), a consumer protection agency, the number of theft reports doubled in the last two years. It makes the detection and prevention of fraudulent activities critically important to financial institutions. Machine learning algorithms provide a proactive mechanism to prevent credit card fraud with acceptable accuracy. In this paper Machine Learning algorithms such as Logistic Regression, Naïve Bayes, Random Forest, K- Nearest Neighbor, Gradient Boosting, Support Vector Machine, and Neural Network algorithms are implemented for detection of fraudulent transactions. A comparative analysis of these algorithms is performed to identify an optimal solution.

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