Credit card fraud is becoming a serious and growing problem as a result of the emergence
of innovative technologies and communication methods, such as contactless payment. In …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Recent advancements in electronic commerce and communication systems have
significantly increased the use of credit cards for both online and regular transactions …

Anomaly detection, aka outlier detection or novelty detection, has been a lasting yet active
research area in various research communities for several decades. There are still some …

The number of Internet of Things (IoT) devices has increased considerably in the past few
years, which resulted in an exponential growth of cyber attacks on IoT infrastructure. As a …

Fraudsters are now more active in their attacks on credit card transactions than ever before.
With the advancement in data science and machine learning, various algorithms have been …

Supervised learning techniques are widely employed in credit card fraud detection, as they
make use of the assumption that fraudulent patterns can be learned from an analysis of past …

New advances in electronic commerce systems and communication technologies have
made the credit card the potentially most popular method of payment for both regular and …

Unsupervised anomaly detection (UAD) attracts a lot of research interest and drives
widespread applications, where only anomaly-free samples are available for training. Some …

Advanced fraud detection systems leverage the digital traces from (credit-card) transactions
to detect fraudulent activity in future transactions. Recent research in fraud detection has …