This paper illustrates an automated system that replicates the investigative operation of human fraud auditors. Human fraud auditors often utilize fraud detection methods that exploit structure in database tables to uncover outliers that may be part of a fraud case. From the uncovered outliers, an auditor will build a case of fraud by searching data related to the outlier possibly across many d...

Fraud is an adaptive crime; special methods of data gathering and analysis are required to combat fraud issues as criminals often quest for dubious techniques to evade detection. Radial basis function (RBF) network, was used to build base models that identifies and detect the risk of fraud in transactions. At first, it is imperative to isolate the basic factors that are predictive of fraud occu...

Corporate fraud these days represents a huge cost to our economy. Academic literature already concentrated on how data mining techniques can be of value in the fight against fraud. All this research focusses on fraud detection, mostly in a context of external fraud. In this paper we discuss the use of a data mining approach to reduce the risk of internal fraud. Reducing fraud risk comprehends b...

This paper proposes an original architecture for a fraud management system (FMS) for convergent. Next-generation networks (NGNs), which are based on the Internet protocol (IP). The architecture has the potential to satisfy the requirements of flexibility and application-independency for effective fraud detection in NGNs that cannot be met by traditional FMSs. The proposed architecture has a tho...

Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier...

GSM (Global Services of Mobile Communications) 1800 licenses were granted in the beginning of the 2000’s in Turkey. Especially in the installation phase of the wireless telecom services, fraud usage can be an important source of revenue loss. Fraud can be defined as a dishonest or illegal use of services, with the intention to avoid service charges. Fraud detection is the name of the activities...

In the last decade, high profile financial frauds committed by large companies in both developed and developing countries were discovered and reported. This study compares the performance of five popular statistical and machine learning models in detecting financial statement fraud. The research objects are companies which experienced both fraudulent and non-fraudulent financial statements betw...

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...

Fraud is a significant problem in many industries, such as banking, insurance, telecommunication, and public service. Detecting and preventing fraud is difficult, because fraudsters develop new schemes all the time, and the schemes grow more and more sophisticated to elude easy detection. Many organizations have implemented fraud detection and prevention systems based on SAS data mining to help...

this paper proposes a HMM (Hidden Markov Model) based fraud detection system for credit card fraud detection. The method works on the statistical behavior of user’s transactions. Since the original transactions are not available due to privacy policies of bank we used here synthetically generated data for a credit card user, and then HMM model is trained using different size of sample of genera...