Credit card fraud is already a significant factor inhibiting consumer confidence in ecommerce. As more advanced payment systems become common, what legal and technological mechanisms are required to ensure that fraud does not do long-term damage to consumers' willingness to use electronic payment mechanisms?

Inductive learning and classification techniques have been applied in many problems in diverse areas. In this paper we describe an AI-based approach that combines inductive learning algorithms and meta-learning methods as a means to compute accurate classification models for detecting electronic fraud. Inductive learning algorithms are used to compute detectors of anomalous or errant behavior o...

With the development of Internet and technology, credit cards are more widely used transaction data larger. The set card fraud is a typical imbalanced problem. model should ensure that detected customer service quality guaranteed. Improving both precision recall rate focus current research. However, when constrained by level machine learning, it good choice to use different models evaluate obta...

Outlier Detection has attracted substantial attention in many applications and research areas. Examples include detection of network intrusions or credit card fraud. Many of the existing approaches are based on pair-wise distances among all points in the dataset. These approaches cannot easily extend to current datasets that usually contain a mix of categorical and continuous attributes, and ma...

Data mining is a combination of database and artificial intelligence technologies. It is a process of identifying and extracting patterns from data, particularly from very large and/or complex sets of data. The major focus of machine learning research is to automatically learn to recognize complex patterns and make intelligent decisions based on data. Data mining and Machine Learning is a relat...

The need for technology has always found space in Financial Transaction as the number of fraud financial transactions increases day by day. In this research we have proposed a new methodology using isolation forest algorithm and local outlier detection to detect fraud. A standard data set is used experimentation classify transaction occurred fraudulent or not. We neural networks machine learnin...

We present a social simulation model that covers three main financial services: Banks, Retail Stores, and Payments systems. Our aim is to address the problem of a lack of public data sets for fraud detection research in each of these domains, and provide a variety of fraud scenarios such as money laundering, sales fraud (based on refunds and discounts), and credit card fraud. Currently, there i...

Despite the widespread attention given to identity theft, there is much confusion on how best to define and measure it. Recent attempts to measure its extent through victimization surveys or law enforcement files have varied considerably in the types of crimes included as identity theft. Some studies include credit card fraud, while others exclude it. This inconsistency in data collection has m...

Financial innovations are a common explanation for the rise in credit card debt and bankruptcies. To evaluate this story, we develop a simple model that incorporates two key frictions: asymmetric information about borrowers’ risk of default and a fixed cost of developing each contract lenders offer. Innovations that ameliorate asymmetric information or reduce this fixed cost have large extensiv...