Open Access
Credit Card Fraud Detection Using Hidden Markov Model
TLDR
It is shown that credit card fraud can be detected using Hidden Markov Model during transactions to obtain a high fraud coverage combined with a low false alarm rate.Citations
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Journal ArticleDOI
How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark
TL;DR: A timely survey of published methods for payment card fraud detection is presented with the focus on methods that use AI and machine learning to show that only eight methods have a practical performance to be deployed in industry despite the body of research.
Journal ArticleDOI
Cost-sensitive payment card fraud detection based on dynamic random forest and k-nearest neighbors
Sanaz Nami,Mehdi Shajari +1 more
TL;DR: The results showed that the recent behavior of cardholders exerts a considerable effect on decision-making regarding the evaluation of transactions as fraudulent or legitimate and that using both primary and derived transactional features increases the F-measure.
Posted Content
Credit card fraud detection using machine learning: A survey
Yvan Lucas,Johannes Jurgovsky +1 more
TL;DR: This survey studies data-driven credit card fraud detection particularities and several machine learning methods to address each of its intricate challenges with the goal to identify fraudulent transactions that have been issued illegitimately on behalf of the rightful card owner.
Journal ArticleDOI
A Survey on Fraud Detection Techniques in Ecommerce
Priya JRana,Jwalant Baria +1 more
TL;DR: This paper shows the approaches used in fraud detection in e-commerce and suggests ways to design potent and efficient fraud detection algorithms for reducing the losses in transaction.
Agent based meta learning in distributed data mining system
TL;DR: Meta-learning and JAM system (Java Agents for Metalearning), which is an agent-based meta-learning system for large-scale data mining applications and a combination of AI-based methods and distributed systems techniques are presented.
References
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Proceedings ArticleDOI
Cost-based modeling for fraud and intrusion detection: results from the JAM project
TL;DR: There is clear evidence that state-of-the-art commercial fraud detection systems can be substantially improved in stopping losses due to fraud by combining multiple models of fraudulent transaction shared among banks.
Journal ArticleDOI
Identifying the signs of fraudulent accounts using data mining techniques
TL;DR: Empirical verification supported that this fraudulent account detection system can successfully identify fraudulent accounts in early stages and is able to provide reference for financial institutions.
Journal ArticleDOI
Distributed data mining in credit card fraud detection
TL;DR: The proposed methods of combining multiple learned fraud detectors under a "cost model" are general and demonstrably useful; the empirical results demonstrate that they can significantly reduce loss due to fraud through distributed data mining of fraud models.
Journal ArticleDOI
Minority report in fraud detection: classification of skewed data
TL;DR: This paper compares the new fraud detection method (meta-learning approach) against C4.5 trained using undersampling, oversamplings, and SMOTEing without partitioning, and shows that, given a fixed decision threshold and cost matrix, the partitioning and multiple algorithms approach achieves marginally higher cost savings than varying the entire training data set with different class distributions.
Proceedings ArticleDOI
Credit card fraud detection with a neural-network
TL;DR: Using data from a credit card issuer, a neural network based fraud detection system was trained on a large sample of labelled credit card account transactions and tested on a holdout data set that consisted of all account activity over a subsequent two-month period of time.
Related Papers (5)
Credit Card Fraud Detection Using Hidden Markov Model and Its Performance
Avinash Ingole,Ravindra C. Thool +1 more