The increasing spread of cyberattacks and crimes makes cyber security a top priority in the
banking industry. Credit card cyber fraud is a major security risk worldwide. Conventional …

The paper contributes to a growing body of empirical work on regulatory technology by
proposing machine learning models to detect fraud in financial markets. The recent spate of …

As technology advanced and e-commerce services expanded, credit cards became one of
the most popular payment methods, resulting in an increase in the volume of banking …

Resource-constrained classification tasks are common in real-world applications such as
allocating tests for disease diagnosis, hiring decisions when filling a limited number of …

Health insurance fraud accounts for 3–10% of total medical expenditures every year. If the
growth of fraud activities is allowed, it will cause irreversible consequences to the medical …

With the rapid expansion of data, the problem of data imbalance has become increasingly
prominent in the fields of medical treatment, finance, network, etc. And it is typically solved …

Protecting financial consumers from investment fraud has been a recurring problem in
Canada. The purpose of this paper is to predict the demographic characteristics of investors …

Credit card payments are one popular e-payment option apart from cash payments. Recent
reports show that credit card fraud and payment defaults are increasing annually and are …

The ease with which mobile money is used to facilitate cross‐border payments presents a
global threat to law enforcement in the fight against money laundering and terrorist …

Stock market prediction (SMP) is a challenging task due to its uncertainty, nonlinearity, and
volatility. Machine learning models, such as artificial neural networks (ANNs) and support …