Despite significant efforts by merchants, card issuers and law enforcement to curb fraud, online fraud continues to plague electronic commerce web sites. More advanced solutions are desired to protect merchants from the constantly evolving problem caused by fraud. The supervised machine learning technique for the most well known fraud detection algorithms makes them inadequate for an online sys...

----------------------------------------------ABSTRACT-------------------------------------------In the faceless world of the Internet, online fraud is one of the greatest reasons of loss for web merchants. Advanced solutions are needed to protect e-businesses from the constant problems of fraud. Many popular fraud detection algorithms require supervised training, which needs human intervention...

A fraud survey found that 75 percent of the 459 responding organizations experienced some form of fraud during the prior 12 month period [1]. The Association of Certified Fraud Examiners (ACFE) estimates 6 percent of revenues are lost due to theft by employees and that small businesses suffer disproportionate losses because of limited resources devoted to fraud detection [2]. Most employee thef...

researchers and practitioners doing work in these three related areas. Risk management, fraud detection, and intrusion detection all involve monitoring the behavior of populations of users (or their accounts) to estimate, plan for, avoid, or detect risk. In his paper, Til Schuermann (Oliver, Wyman, and Company) categorizes risk into market risk, credit risk, and operating risk (or fraud). Simil...

In recent years, due to the expansion of financial institutions,as well as the popularity of the World Wide Weband e-commerce, a significant increase in the volume offinancial transactions observed. In addition to the increasein turnover, a huge increase in the number of fraud by user’sabnormality is resulting in billions of dollars in lossesover the world. T...

This paper discusses the development and research for the detection of fraud in Smart-Card environments by using artificial intelligence. The current research deals with behaviour based detection engine, which will detect abnormalities by learning the usual behaviour of the user and detecting new unusual behaviours. The behaviour-based detection engines is based on ‘Neural Networks’. This work ...

Periodic patterns often present on document images as holograms, watermarks or guilloche elements which are mostly used for fraud protection. Automatic detection of such patterns allows the automatic document processing system to perform fraud detection, helps determine document types and lets an embedded OCR system to vary its settings depending on pattern presence. In this paper we propose a ...

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...

In parallel to the increase in the number of credit card transactions, the financial losses due to fraud have also increased. Thus, the popularity of credit card fraud detection has been increased both for academicians and banks. Many supervised learning methods were introduced in credit card fraud literature some of which bears quite complex algorithms. As compared to complex algorithms which ...

Fraud and security continue to be problems for firms. Fraud information is typically incomplete and deliberately obfuscated. These qualities make fraud events harder to detect using conventional security controls. This study uses a knowledge management framework to explore how different types of controls are used to detect and investigate information fraud. The analysis is based on the customer...