Background The increased adoption of the internet, social media, wearable devices, e-
health services, and other technology-driven services in medicine and healthcare has led to …

With the rise of technology and the continued economic growth evident in modern society,
acts of fraud have become much more prevalent in the financial industry, costing institutions …

Novelty detection, ie, identifying whether a given sample is drawn from outside the training
distribution, is essential for reliable machine learning. To this end, there have been many …

This paper gives a comprehensive revision of the state-of-the-art research in detecting
financial fraud from 2009 to 2019 inclusive and classifying them based on their types of …

Anomaly detection in high dimensional data is becoming a fundamental research problem
that has various applications in the real world. However, many existing anomaly detection …

Machine learning is predominantly an area of Artificial Intelligence which has been a key
component of digitalization solutions that has caught major attention in the digital arena. In …

Computational intelligence in finance has been a very popular topic for both academia and
financial industry in the last few decades. Numerous studies have been published resulting …

Outlier detection is a statistical procedure that aims to find suspicious events or items that
are different from the normal form of a dataset. It has drawn considerable interest in the field …

We present a two-stage framework for deep one-class classification. We first learn self-
supervised representations from one-class data, and then build one-class classifiers on …

We give an overview of recent exciting achievements of deep reinforcement learning (RL).
We discuss six core elements, six important mechanisms, and twelve applications. We start …