Utilizing GANs for fraud detection: model training with synthetic transaction data
M Zhu, Y Gong, Y Xiang, H Yu… - … Conference on Image …, 2024 - spiedigitallibrary.org
Anomaly detection is a critical challenge across various research domains, aiming to identify
instances that deviate from normal data distributions. This paper explores the application of …
instances that deviate from normal data distributions. This paper explores the application of …
[PDF][PDF] Enhancing cyber financial fraud detection using deep learning techniques: a study on neural networks and anomaly detection
OA Bello, A Folorunso, A Ogundipe… - … Journal of Network …, 2022 - researchgate.net
In the rapidly evolving landscape of cyber financial fraud, traditional detection methods are
increasingly inadequate to counter sophisticated fraudulent activities. This study examines …
increasingly inadequate to counter sophisticated fraudulent activities. This study examines …
Credit Card Fraud Detection via Intelligent Sampling and Self-supervised Learning
The significant increase in credit card transactions can be attributed to the rapid growth of
online shopping and digital payments, particularly during the COVID-19 pandemic. To …
online shopping and digital payments, particularly during the COVID-19 pandemic. To …
Examining the ability of big data analytics to investigate financial reporting quality: a comprehensive bibliometric analysis
A Aboelfotoh, AM Zamel, AA Abu-Musa… - Journal of Financial …, 2024 - emerald.com
Purpose This study aims to examine the ability of big data analytics (BDA) to investigate
financial reporting quality (FRQ), identify the knowledge base and conceptual structure of …
financial reporting quality (FRQ), identify the knowledge base and conceptual structure of …
[HTML][HTML] Balancing act: Tackling organized retail fraud on e-commerce platforms with imbalanced learning text models
As online shopping expands rapidly, so does the prevalence of fraud, resulting in significant
losses for retailers. According to the 2020 National Retail Federation (NRF) report …
losses for retailers. According to the 2020 National Retail Federation (NRF) report …
[HTML][HTML] Generative Adversarial Networks in Business and Social Science
A Ruiz-Gándara, L Gonzalez-Abril - Applied Sciences, 2024 - mdpi.com
Featured Application The importance of generative adversarial networks (GANs) in
economics is growing and is driven by successes in other fields. Many economic problems …
economics is growing and is driven by successes in other fields. Many economic problems …
UCF-PKS: Unforeseen Consumer Fraud Detection With Prior Knowledge and Semantic Features
S Lai, J Wu, C Ye, Z Ma - IEEE Transactions on Computational …, 2024 - ieeexplore.ieee.org
The utilization of text classification techniques has demonstrated great promise in the field of
detecting consumer fraud based on consumer reviews. However, persistent challenges …
detecting consumer fraud based on consumer reviews. However, persistent challenges …
Fund transfer fraud detection: Analyzing irregular transactions and customer relationships with self-attention and graph neural networks
YC Shih, TS Dai, YP Chen, YW Ti, WH Wang… - Expert Systems with …, 2024 - Elsevier
This paper presents a method for identifying fraudulent fund transfers using real bank data,
analyzing customer information, transactional activities, and customer relationships. The …
analyzing customer information, transactional activities, and customer relationships. The …
CFTNet: a robust credit card fraud detection model enhanced by counterfactual data augmentation
M Kong, R Li, J Wang, X Li, S Jin, W Xie, M Hou… - Neural Computing and …, 2024 - Springer
Establishing a reliable credit card fraud detection model has become a primary focus for
academia and the financial industry. The existing anti-fraud methods face challenges related …
academia and the financial industry. The existing anti-fraud methods face challenges related …
Self-supervised enhanced denoising diffusion for anomaly detection
S Li, J Yu, Y Lu, G Yang, X Du, S Liu - Information Sciences, 2024 - Elsevier
Generative models have significantly enhanced anomaly detection through their powerful
ability to model data. However, many existing generative model-based anomaly detection …
ability to model data. However, many existing generative model-based anomaly detection …