Collecting insurance fraud samples is costly and if performed manually is very time consuming. This issue suggests usage of unsupervised models. One of the accurate methods in this regards is Spectral Ranking of Anomalies (SRA) that is shown to work better than other methods for auto insurance fraud detection specifically. However, this approach is not scalable to large samples and is not appro...

With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credi...

In this paper a fraud detection method is proposed which user behaviors are modeled using two main components namely the un-normal trend analysis component and scenario based component. The extent of deviation of a transaction from his/her normal behavior is estimated using fuzzy membership functions. The results of applying all membership functions on a transaction will then be infused and a f...

One method for detecting fraud is to check for suspicious changes in user behavior over time. This paper describes the automatic design of user profiling methods for the purpose of fraud detection, using a series of data mining and machine learning techniques. It uses a rule-learning program to uncover indicators of fraudulent behavior from a large database customer transactions. Then the indic...

Title of dissertation: Securities Fraud: An Economic Analysis Yue Wang, Doctor of Philosophy, 2005 Dissertation directed by: Professor Lemma Senbet, Professor Nagpurnanand Prabhala Department of Finance This thesis develops an economic analysis of securities fraud. The thesis consists of a theory essay and an empirical essay. In the theory essay, I analyze a firm’s propensity to commit securiti...

The paper presents fraud detection method to predict and analyze fraud patterns from data. To generate classifiers, we apply the Naïve Bayesian Classification, and Decision Tree-Based algorithms. A brief description of the algorithm is provided along with its application in detecting fraud. The same data is used for both the techniques. We analyze and interpret the classifier predictions. The m...

As online commerce becomes more common, fraud is an increasingly important concern. Current anti-fraud techniques include blacklists, hand-crafted rules, and review of individual transactions by human experts. These methods have achieved good results, but a significant number of costly fraudulent transactions still occur, and the fraud detection process is expensive due to its heavy reliance on...

This paper reports an experiment aimed at generating synthetic test data for fraud detection in an IP based videoon-demand service. The data generation verifies a methodology previously developed by the present authors [7] that ensures that important statistical properties of the authentic data are preserved by using authentic normal data and fraud as a seed for generating synthetic data. This ...

Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. The implementation of data mining techniques for fraud detection follows the traditional information flow of data mining, which begins with feature selection followed by representation, da...

Researchers and practitioners have made many attempts to identify fraud indicators and to build fraud prediction procedures. Fraud is a beg subject and causes tremendous loss to the business world and creates morale problems in the workplace. It represents a critical issue for the accounting profession, in devolved and non-developed countries. Numerous surveys in the 1980s have served to underl...