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 …

This paper surveys the extant literature on machine learning, artificial intelligence, and deep
learning mechanisms within the financial sphere using bibliometric methods. We considered …

Supervised machine learning algorithms have been a dominant method in the data mining
field. Disease prediction using health data has recently shown a potential application area …

Detecting frauds in credit card transactions is perhaps one of the best testbeds for
computational intelligence algorithms. In fact, this problem involves a number of relevant …

This authoritative, expanded and updated second edition of Encyclopedia of Machine
Learning and Data Mining provides easy access to core information for those seeking entry …

The numerous recent breakthroughs in machine learning make imperative to carefully
ponder how the scientific community can benefit from a technology that, although not …

Credit card transaction fraud costs billions of dollars to card issuers every year. A well-
developed fraud detection system with a state-of-the-art fraud detection model is regarded …

Financial fraud is an issue with far reaching consequences in the finance industry,
government, corporate sectors, and for ordinary consumers. Increasing dependence on new …

Outliers are also referred to as abnormalities, discordants, deviants, or anomalies in the data
mining and statistics literature. In most applications, the data is created by one or more …

Anomaly detection is an important data analysis task. It is used to identify interesting and
emerging patterns, trends and anomalies from data. Anomaly detection is an important tool …