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 …

In the rapidly evolving landscape of cyber financial fraud, traditional detection methods are
increasingly inadequate to counter sophisticated fraudulent activities. This study examines …

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 …

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 …

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 …

Featured Application The importance of generative adversarial networks (GANs) in
economics is growing and is driven by successes in other fields. Many economic problems …

The utilization of text classification techniques has demonstrated great promise in the field of
detecting consumer fraud based on consumer reviews. However, persistent challenges …

This paper presents a method for identifying fraudulent fund transfers using real bank data,
analyzing customer information, transactional activities, and customer relationships. The …

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 …

Generative models have significantly enhanced anomaly detection through their powerful
ability to model data. However, many existing generative model-based anomaly detection …