This article presents fraudulent activity that are done by the unscrupulous desire of people to make the personal benefits by manipulating the tax in taxing system. Taxpayers manipulate the money paid to the tax authorities through avoidance and evasion activities. In this paper, we deal with a specific technique used by the tax-evaders known as a circular trading. We define an algorithm for detection and analysis of circular trade. To detect these circular trade, we have modeled whole system as a directed graph with actors being vertices and the transactions among them as directed edges. We have proposed an algorithm for detecting these circular trade. The commercial tax dataset is given by Telangana, India. This dataset contains the transaction details of participants involved in a known circular trade.
Many mal-practices in stock market trading--e.g., circular trading and price manipulation--use the modus operandi of collusion. Informally, a set of traders is a candidate collusion set when they have "heavy trading" among themselves, as compared to their trading with others. We formalize the problem of detection of collusion sets, if any, in the given trading database. We show that naive approaches are inefficient for real-life situations. We adapt and apply two well-known graph clustering algorithms for this problem. We also propose a new graph clustering algorithm, specifically tailored for detecting collusion sets. A novel feature of our approach is the use of Dempster---Schafer theory of evidence to combine the candidate collusion sets detected by individual algorithms. Treating individual experiments as evidence, this approach allows us to quantify the confidence (or belief) in the candidate collusion sets. We present detailed simulation experiments to demonstrate effectiveness of the proposed algorithms. Market manipulation remains the biggest concern of investors in today's securities market, despite fast and strict responses from regulators and exchanges to market participants that pursue such practices. The existing methods in the industry for detecting fraudulent activities in securities market rely heavily on a set of rules based on expert knowledge. The securities market has deviated from its traditional form due to new technologies and changing investment strategies in the past few years. The current securities market demands scalable machine learning algorithms supporting identification of market manipulation activities. In this paper we use supervised learning algorithms to identify suspicious transactions in relation to market manipulation in stock market. We use a case study of manipulated stocks during 2003. We adopt CART, conditional inference trees, C5.0, Random Forest, Naive Bayes, Neural Networks, SVM and kNN for classification of manipulated samples. Empirical results show that Naive Bayes outperform other learning methods achieving F 2 measure of 53 (sensitivity and specificity are 89 and 83 respectively). In financial markets, abnormal trading behaviors pose a serious challenge to market surveillance and risk management. What is worse, there is an increasing emergence of abnormal trading events that some experienced traders constitute a collusive clique and collaborate to manipulate some instruments, thus mislead other investors by applying similar trading behaviors for maximizing their personal benefits. In this paper, a method is proposed to detect the potential collusive cliques involved in an instrument of future markets by first calculating the correlation coefficient between any two eligible unified aggregated time series of signed order volume, and then combining the connected components from multiple sparsified weighted graphs constructed by using the correlation matrices where each correlation coefficient is over a user-specified threshold. Experiments conducted on real order data from the Shanghai Futures Exchange show that the proposed method can effectively detect suspect collusive cliques, which have been verified by financial experts. A tool based on the proposed method has been deployed in the exchange as a pilot application for futures market surveillance and risk management.