Real-time Credit Card Fraud Detection Using Machine Learning

Thennakoon, Anuruddha; Bhagyani, Chee; Premadasa, Sasitha; Mihiranga, Shalitha; Kuruwitaarachchi, N.

doi:10.1109/confluence.2019.8776942

Cited by 143 publications

(43 citation statements)

References 12 publications

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“…Thennakoon et al [8] state that in today's era of technology especially in the Internet commerce and banking, the transactions by the Mastercards have been increasing rapidly. The Mastercard becomes the highly useable equipment for Internet shopping.…”

Section: Literature Surveymentioning

confidence: 99%

An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

Sanober

Alam

Pande

et al. 2021

Wireless Communications and Mobile Computing

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In today’s era of technology, especially in the Internet commerce and banking, the transactions done by the Mastercards have been increasing rapidly. The card becomes the highly useable equipment for Internet shopping. Such demanding and inflation rate causes a considerable damage and enhancement in fraud cases also. It is very much necessary to stop the fraud transactions because it impacts on financial conditions over time the anomaly detection is having some important application to detect the fraud detection. A novel framework which integrates Spark with a deep learning approach is proposed in this work. This work also implements different machine learning techniques for detection of fraudulent like random forest, SVM, logistic regression, decision tree, and KNN. Comparative analysis is done by using various parameters. More than 96% accuracy was obtained for both training and testing datasets. The existing system like Cardwatch, web service-based fraud detection, needs labelled data for both genuine and fraudulent transactions. New frauds cannot be found in these existing techniques. The dataset which is used contains transaction made by credit cards in September 2013 by cardholders of Europe. The dataset contains the transactions occurred in 2 days, in which there are 492 fraud transactions out of 284,807 which is 0.172% of all transaction.

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Section: Literature Surveymentioning

confidence: 99%

An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

Sanober

Alam

Pande

et al. 2021

Wireless Communications and Mobile Computing

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“…Normalization may involve various methods such as min-max, decimal scaling, Z-score etc. Credit Card Fraud Detection [6], [8] involves immense research to find out the fraud cases by using Machine Learning [5], [26] and Data Mining [7], [27] as major fields. Analysis of Credit Card Fraud detection [2], [12] involves continuous monitoring of performance of a given model.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

2021

IJETER

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With the advent of modern transaction technology, many are using online transactions to transfer money from one person to another. Credit Card Fraud, a rising problem in the financial department goes unnoticed most of the time. A lot of research is going on in this area.The Credit Card Fraud Detection project is developed to spot whether a new transaction is fraudulent or not with the knowledge of previousdata. We use various predictive models to ascertain how accurate they are in predicting whether a transaction is abnormalor regular. Techniques like Decision Tree, Logistic Regression, SVMand Naïve Bayes are the classification algorithms to detect non-fraud and fraud transactions.

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“…The result shows that Decision Tree and SVM algorithms can perform well even if the data set is imbalanced. Based on real-life credit card data, Support Vector Machine, Naive Bayes, K-Nearest Neighbor, and Logistic Regression are evaluated for credit card fraud detection [7].…”

Section: Related Workmentioning

confidence: 99%

Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse

et al. 2021

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Online frauds have pernicious impacts on different system domains, including waste management systems. Fraudsters illegally obtain rewards for their recycling activities or avoid penalties for those who are required to recycle their own waste. Although some approaches have been introduced to prevent such fraudulent activities, the fraudsters continuously seek new ways to commit illegal actions. Machine learning technology has shown significant and impressive results in identifying new online fraud patterns in different system domains such as e-commerce, insurance, and banking. The purpose of this paper, therefore, is to analyze a waste management system and develop a machine learning model to detect fraud in the system. The intended system allows consumers, individuals, and organizations to track, monitor, and update their performance in their recycling activities. The data set provided by a waste management organization is used for the analysis and the model training. This data set contains transactions of users’ recycling activities and behaviors. Three machine learning algorithms, random forest, support vector machine, and multi-layer perceptron are used in the experiments and the best detection model is selected based on the model’s performance. Results show that each of these algorithms can be used for fraud detection in waste managements with high accuracy. The random forest algorithm produces the optimal model with an accuracy of 96.33%, F1-score of 95.20%, and ROC of 98.92%.

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Real-time Credit Card Fraud Detection Using Machine Learning

Cited by 143 publications

References 12 publications

An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

An Enhanced Secure Deep Learning Algorithm for Fraud Detection in Wireless Communication

Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets

Waste Management System Fraud Detection Using Machine Learning Algorithms to Minimize Penalties Avoidance and Redemption Abuse

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