Social Media (SM) are the most widespread and rapid data generation applications on the
Internet increase the study of these data. However, the efficient processing of such massive …

Credit risk is the risk of financial loss when a borrower fails to meet the financial commitment.
While there are many factors that constitute credit risk, due diligence while giving loan (credit …

This study proposes a framework for fraud detection in the auto insurance industry by using
predictive models. The feature selection is performed utilizing a publicly available car …

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 …

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 …

Compared to traditional machine learning, deep learning can learn deeper abstract data
representation and understand scattered data properties. It has gained considerable …

As credit card becomes the most popular payment mode particularly in the online sector, the
fraudulent activities using credit card payment technologies are rapidly increasing as a …

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 …

In order to improve the performance of any machine learning model, it is important to focus
more on the data itself instead of continuously develo** new algorithms. This is exactly the …