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Showing 1 to 9 of 9 for “"Credit Card Fraud Detection"”.
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Context-Aware Credit Card Fraud Detection
Credit card fraud has emerged as major problem in the electronic payment sector. In this thesis, we study data-driven fraud detection and address several of its intricate challenges by means of machine learning methods with the goal to identify fraudulent transactions that have been issued …
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Using Context for Credit Card Fraud Detection
Online payment fraud is one of the biggest challenges accompanying the ubiquitous adoption of digital payment methods. The academic literature shows that providing data-driven models with additional context of a transaction results in significant improvements in fraud detection performance. …
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Credit card fraud detection using incremental feature learning
Detecting credit card fraud is essential and it is one of the most popular payment methods. Credit card fraud can cause huge losses for cardholders. Therefore, so many studies have focused on proposing different standard machine learning methods and limited use of incremental learning to create a …
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Modelling highly imbalanced credit card fraud detection data using statistical learning
Credit card fraud is a major concern for businesses worldwide, yielding losses of up to $67 billion per year in major banks and institutions. Machine learning techniques used to detect fraudulent transactions face several challenges when dealing with highly imbalanced data, which is often the case …
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Credit card fraud detection using machine learning with integration of contextual knowledge
… In fact, we model the authentic and fraudulent behavior of merchants and card holders according to two univariate characteristics: the date and the amount of transactions. In addition, attributes based on HMMs are created in a supervised manner, thereby reducing the need for expert …
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Optimising credit card fraud detection through machine learning and deep learning with spatial-temporal imbalance handling
… on digital payment systems and raised financial fraud, highlighting the need for effective fraud detection systems. This research addresses the "class imbalance challenge" in credit card fraud detection by integrating geolocation and temporal analysis to improve trend identification and anomaly …
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RESONANT: Reinforcement Learning Based Moving Target Defense for Detecting Credit Card Fraud
According to security.org, as of 2023, 65% of credit card (CC) users in the US have been subjected to fraud at some point in their lives, which equates to about 151 million Americans. The proliferation of advanced machine learning (ML) algorithms has also contributed to detecting credit card fraud …
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A Constrained Box Algorithm for Imbalanced Data in Remote Sensing Images
… problem can be found in many domains such as credit card fraud detection and rare diseases diagnosis.</p> <p>Imbalanced data is a prominent issue also in remote sensing images (RSI) which are used to obtain information of earth resources and the surrounding environment. RSI are collected by …
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One-class classification in the presence of point, collective, and contextual anomalies
Anomaly detection has a prominent position in the processing pipeline of any real-world data-driven application. Its central goal is to detect and separate valid data points from malicious-anomalous-ones such that the cleaned data set can be processed further. In many applications, anomalies are …