Credit card fraud is becoming a serious and growing problem as a result of the emergence
of innovative technologies and communication methods, such as contactless payment. In …

Credit card fraud is a significant concern for financial institutions and cardholders alike. As
fraudulent activities become more sophisticated, traditional rule-based approaches struggle …

The strict non-stationary time series (NS-TS) forecasting is one of the challenging tasks as
the series does not follow any defined pattern. Previous studies had mainly focused on …

Fraud detection systems support advanced detection techniques based on complex rules,
statistical modelling and machine learning. However, alerts triggered by these systems still …

Supervised learning techniques typically assume training data originates from the target
population. Yet, in reality, dataset shift frequently arises, which, if not adequately taken into …

The state-of-the-art feature-engineering method for fraud classification of electronic
payments uses manually engineered feature aggregates, ie, descriptive statistics of the …

Abstract Model selection is a highly important step in the process of extracting knowledge
from datasets. This is usually done via partitioning strategies such as cross-validation in …

Credit card payment is increasing day by day. This leads to expanding digitization of
banking administrations and versatile financial applications. A large number of transactions …

Enhancement of technology yields more complex time-dependent outcomes for better
understanding and analysis. These outcomes generate more complex, unstable, and …

This research study analyzes the different data mining techniques used for the detection of
credit card frauds. Additionally, different data mining techniques are properly described in …