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 ...

The COVID-19 pandemic has caused nationwide economic losses. This condition forces companies to try keeping financial performance stable. What needs be watched out for during this is hiding or manipulating company statements improve investors’ interest in investing on the pandemic. Before pandemic, statement fraud cases had often occurred Indonesia, such as of auditor failure, revising statemen...

With the rapid growth of the netizen population in China, more and more internet frauds are committed. Many people suffer from internet frauds by losing wealth or other valuable things. To prevent internet frauds, we first need to discover the methods in which internet frauds are conducted. In this paper, we investigate and categorize the internet frauds in China. So far, there are typically si...

Large enterprises frequently enforce accounting limits to reduce the impact of fraud. As a complement to accounting limits, auditors use Benford analysis to detect traces of undesirable or illegal activities in accounting data. Unfortunately, the two fraud fighting measures often do not work well together. Accounting limits may significantly disturb the digit distribution examined by Benford an...

Fraud compromises the thriving Internet auction market. Studies have shown that fraudsters often manipulate their reputations through sophisticated collusions with accomplices, enabling them to defeat the reputation-based feedback systems that are used to combat fraud. This paper presents an algorithm that can identify colluding fraudsters in real time. Experiments with eBay transaction data sh...

This paper presents a procedure for estimating VAR using Sequential Discounting VAR (SDVAR) algorithm for online model learning to detect fraudulent acts using the telecommunications call detailed records (CDR). The volatility of the VAR is observed allowing for non-linearity, outliers and change points based on the works of [1]. This paper extends their procedure from univariate to multivariat...

Given the retweeting activity for the posts of several Twitter users, how can we distinguish organic activity from spammy retweets by paid followers to boost a post’s appearance of popularity? More generally, given groups of observations, can we spot strange groups? Our main intuition is that organic behavior has more variability, while fraudulent behavior, like retweets by botnet members, is m...

T his paper provides a methodology for detecting management fraud using basic financial data. The methodology is based on support vector machines. An important aspect therein is a kernel that increases the power of the learning machine by allowing an implicit and generally nonlinear mapping of points, usually into a higher dimensional feature space. A kernel specific to the domain of finance is...

This paper discusses the current status of research on fraud detection undertaken as part of the European Commissionfunded ACTS ASPeCT (Advanced Security for Personal Communications Technologies) project, by Royal Holloway University of London. Using a recurrent neural network technique, we uniformly distribute prototypes over Toll Tickets, sampled from the U.K. network operator, Vodafone. The ...