Billions of dollars of loss are caused every year due to fraudulent credit card transactions. The design of efficient fraud detection algorithms is key for reducing these losses, and more and more algorithms rely on advanced machine learning techniques to assist fraud investigators. The design of fraud detection algorithms is however particularly challenging due to non stationary distribution o...

The use of credit cards is of paramount importance in improving the economic strength of any nation, however, fraudulent activities associated with it is of great concern. When fraud occurs on credit cards, the negative impact is huge as the financial loss experienced cuts across all the parties involved. This paper provides a proactive measure at detecting fraudulent activities regarding the c...

The use of credit cards is of paramount importance in improving the economic strength of any nation, however, fraudulent activities associated with it is of great concern. When fraud occurs on credit cards, the negative impact is huge as the financial loss experienced cuts across all the parties involved. This paper provides a proactive measure at detecting fraudulent activities regarding the c...

The use of credit cards is of paramount importance in improving the economic strength of any nation, however, fraudulent activities associated with it is of great concern. When fraud occurs on credit cards, the negative impact is huge as the financial loss experienced cuts across all the parties involved. This paper provides a proactive measure at detecting fraudulent activities regarding the c...

The use of credit cards is of paramount importance in improving the economic strength of any nation, however, fraudulent activities associated with it is of great concern. When fraud occurs on credit cards, the negative impact is huge as the financial loss experienced cuts across all the parties involved. This paper provides a proactive measure at detecting fraudulent activities regarding the c...

We introduce a new approach to anomaly detection based on extreme value theory statistics. Our method improves detection accuracy by replacing binary feature thresholds with anomaly scores and by modeling the tail region of the distribution where anomalies occur. It requires no optimization or tuning and provides insights into results. This work describes the Extreme Value Theory-Anomaly Detect...

Previous analysis has shown that applying Bayes minimum risk to detect credit card fraud leads to better results measured by monetary savings, compared with traditional methodologies. Nevertheless, this approach requires good probability estimates that not only separates well between positive and negative examples, but also assesses the real probability of the event. Unfortunately not all class...

Credit evaluation is one of the most important and difficult tasks for credit card companies, mortgage companies, banks and other financial institutes. Incorrect credit judgement causes huge financial losses. This work describes the use of an evolutionary-fuzzy system capable of classifying suspicious and non-suspicious credit card transactions. The paper starts with the details of the system u...

As in present scenario the credit cards or netbanking is very popular and most preferred mode of transaction.The security of these transaction is also a major issue.In this paper we have given the theory to use three key factors of check on any transaction which is firstly trained by the HMM.This is to make the transactions more secure than the previously given theories.We firstly create the be...

Credit card fraud detection based on machine learning has recently attracted considerable interest from the research community. One of the most important tasks in this area is the ability of classifiers to handle the imbalance in credit card data. In this scenario, classifiers tend to yield poor accuracy on the fraud class (minority class) despite realizing high overall accuracy. This is due to...