People can use credit cards for online transactions as it provides an efficient and easy-to-use facility. With the increase in usage of cards, capacity card misuse has also enhanced. Credit frauds cause significant financial losses both holders companies. In this research study, main aim is to detect such frauds, including accessibility public data, high-class imbalance changes fraud nature, hi...

This paper studies the performance analysis of machine learning (ML) and data mining techniques for anomaly detection in credit cards. As usage digital money or plastic grows developing nations, so does risk fraud. To counter these scams, we need a sophisticated fraud method that not only identifies but also detects it before occurs efficiently. We have introduced notion card its many variants ...

The rapid development of technology has digitized customer payment behavior towards a cashless society. To certain extent, this created feast for miscreants to commit fraud. According Nilson (2020), global fraud loss is projected reach over $\$ $ </inl...

In many industrialized and developing nations, credit cards are one of the most widely used methods payment for online transactions. Credit card invention has streamlined, facilitated, enhanced internet It has, however, also given criminals more opportunities to commit fraud, which raised rate fraud. fraud a concerning global impact; businesses ordinary users have lost millions US dollars as re...

An outlier is an observation (or measurement) that is different with respect to the other values contained in a given dataset. Outliers can be due to several causes. The measurement can be incorrectly observed, recorded or entered into the process computer, the observed datum can come from a different population with respect to the normal situation and thus is correctly measured but represents ...

Credit cards play an essential role in today’s digital economy, and their usage has recently grown tremendously, accompanied by a corresponding increase credit card fraud. Machine learning (ML) algorithms have been utilized for fraud detection. However, the dynamic shopping patterns of holders class imbalance problem made it difficult ML classifiers to achieve optimal performance. In order solv...

As the usage of credit card has increased the credit card fraud has also increased dramatically. Existing fraud detection techniques are not capable to detect fraud at the time when transaction is in progress. Improvement in existing fraud detection is necessary. In this paper Hidden Markov model is used to detect the fraud when transaction is in progress. Here is shown that hidden markov model...

Outlier detection is useful for credit card fraud detection. Due to drastic increase in digital frauds, there is a lot of financial losses and therefore various techniques are developed for fraud detection and applied to diverse business fields. In high-dimensional data, outlier detection presents some challenges because of increment of dimensionality. In this paper, the proposed model aims to ...

This paper proposes an intelligent credit card fraud detection model for detecting fraud from highly imbalanced and anonymous credit card transaction datasets. The class imbalance problem is handled by finding legal as well as fraud transaction patterns for each customer by using frequent itemset mining. A matching algorithm is also proposed to find to which pattern (legal or fraud) the incomin...