Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 66 for “"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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Revisiting Fraud Detection From The Language Of Financial Reports
Financial statement fraud has well-documented adverse effects on investors and the broader economy. The seriousness of this issue has emphasized the necessity for advanced detection methods, and one promising approach is utilizing machine learning to analyze qualitative disclosures in corporate …
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Enhancing the auditor's fraud detection ability: An interdisciplinary approach
… auditors and financial statement users is fraud-detection by auditors. The ability of auditors to detect material irregularities, including fraud, should be enhanced to enable them to apply "reasonable skill and care" in carrying out the audit. Such proficiency in fraud detection is needed …
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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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A replication of a cost-sensitive decision tree approach for fraud detection
Payment card fraud poses a problem with widespread implications. However, the class imbalance between genuine and fraudulent transactions presents a challenge to traditional learning methods. Cost-based methods address this problem by assigning different costs to the misclassification of each …
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THE INVISIBLE FRAUD: THE IMPACT OF INATTENTIONAL BLINDNESS ON AUDITOR FRAUD DETECTION
Evidence gathered from major fraud investigations over the last decade has revealed that auditors in these cases failed to attend to fraud red flags within the substantive testing evidence. Research in psychology regarding inattentional blindness (IB) provides a theoretical framework for explaining …
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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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Cross channel fraud detection framework in financial services using recurrent neural networks
The reliability and performance of real time fraud detection techniques has been a major concern for the financial institutions as traditional fraud detection models couldn’t cope with the emerging new and innovative attacks that deceive banks. The problems are further exacerbated with evolving …
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Vigilis: Leveraging Language Models for Fraud Detection in Mobile Communications and Financial Transactions
… measures. To address this, we propose Vigilis, a fraud-protected application that employs advanced language models to counter such attacks in calls, texts, and payments. We first collect and make available a corpus of fraudulent calls from the Internet and train lightweight transformer-based …
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Data mining, fraud detection and mobile telecommunications: call pattern analysis with unsupervised neural networks
… of customer care and retention, marketing and fraud detection. One of the strategies for fraud detection checks for signs of questionable changes in user behavior. Although the intentions of the mobile phone users cannot be observed, their intentions are reflected in the call data which define …
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Strategic Sampling: A Framework for Enhancing Speed and Performance of Financial Fraud Detection Models
Financial fraud detection is a high-stakes field where rapid inference is essential. While state-of-the-art fraud detection models vary in terms of architectural decisions and appear to exhibit unique computational bottlenecks, we highlight that their run-times are all dominated by extensive …
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The auditor and fraud detection : an interpretation of the Companies Acts from 1844 to 1989.
… on understanding the role of the auditor towards fraud detection. More specifically, it is concerned with ascertaining the statutory audit objectives (relating to fraud detection) from all the relevant Companies Acts since 1844. In addition, it offers some sociological interpretations of the …
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Data-driven methods to improve resource utilization, fraud detection, and cyber-resilience in smart grids
… methods, improve resource utilization, fraud detection, and cyber-resilience in smart grids. The modern power grid, known as the smart grid, uses computer communication networks to improve efficiency by transporting control and monitoring messages between devices. At a high level, those …
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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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A new feature engineering framework for financial cyber fraud detection using machine learning and deep learning
… in the United Kingdom have increased because fraudulent techniques have also progressed and used advanced technology. Using traditional fraud detection models with only raw transaction data cannot cope with the emerging new and innovative scheme to deceive financial institutions. Many studies …
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