The great losses caused by financial fraud have attracted continuous attention from
academia, industry, and regulatory agencies. More concerning, the ongoing coronavirus …

Fraud entails deception in order to obtain illegal gains; thus, it is mainly evidenced within
financial institutions and is a matter of general interest. The problem is particularly complex …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Intrusion Detection Systems (IDSs) play a vital role in securing today's Data-Centric
Networks. In a dynamic environment such as the Internet of Things (IoT), which is vulnerable …

The term credit scoring is extensively used in credit industries for decision making and
measuring the risk associated with an applicant. It uses applicants' historical data for credit …

Abstract Machine learning algorithms have come to dominate several industries. After
decades of resistance from examiners and auditors, machine learning is now moving from …

In the last decades Machine Learning achieved notable results in various areas of data
processing and classification, which made the creation of real-time interactive and intelligent …

Outlier detection is to separate anomalous data from inliers in the dataset. Recently, the
most deep learning methods of outlier detection leverage an auxiliary reconstruction task by …

Consumer fraud in online shopping has become a major problem and severe challenge for
online retailers. However, detection lags behind—for academia and practice—and data …

One-Class anomaly detection aims to detect anomalies from normal samples using a model
trained on normal data. With recent advancements in deep learning, researchers have …