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.
Most of the work on @math are concentrated on stock market trading. In @cite_0 , @cite_5 , @cite_6 , @cite_2 and @cite_14 the authors have investigated on @math and other related collusion techniques used in stock market trading. A brief overview on some of these techniques is given below.
@cite_14 @cite_6 @cite_0 @cite_2 @cite_5
2016647770 1542569948 2284153934 1985943920
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. In this paper, we analyze the trading behavior of users in an experimental stock market with a special emphasis on irregularities within the set of regular trading operations. To this end the market is represented as a graph of traders that are connected by their transactions. Our analysis is executed from two perspectives: On a micro scale view fraudulent transactions between traders are introduced and described in terms of the patterns they typically produce in the market’s graph representation. On a macro scale, we use a spectral clustering method based on the eigensystem of complex Hermitian adjacency matrices to characterize the trading behavior of the traders and thus characterize the market. Thereby, we can show the gap between the formal definition of the market and the actual behavior within the market where deviations from the allowed trading behavior can be made visible. These questions are for instance relevant with respect to the forecast efficiency of experimental stock markets since manipulations tend to decrease the precision of the market’s results. To demonstrate this we show some results of the analysis of a political stock market that was set up for the 2006 state parliament elections in Baden-Wuerttemberg, Germany. We propose a survey of soft computing techniques applied to financial market.We surveyed several primary studies proposed in the literature.A framework for building an intelligent trading system was proposed.Future directions of this research field are discussed. Financial markets play an important role on the economical and social organization of modern society. In these kinds of markets, information is an invaluable asset. However, with the modernization of the financial transactions and the information systems, the large amount of information available for a trader can make prohibitive the analysis of a financial asset. In the last decades, many researchers have attempted to develop computational intelligent methods and algorithms to support the decision-making in different financial market segments. In the literature, there is a huge number of scientific papers that investigate the use of computational intelligence techniques to solve financial market problems. However, only few studies have focused on review the literature of this topic. Most of the existing review articles have a limited scope, either by focusing on a specific financial market application or by focusing on a family of machine learning algorithms. This paper presents a review of the application of several computational intelligent methods in several financial applications. This paper gives an overview of the most important primary studies published from 2009 to 2015, which cover techniques for preprocessing and clustering of financial data, for forecasting future market movements, for mining financial text information, among others. The main contributions of this paper are: (i) a comprehensive review of the literature of this field, (ii) the definition of a systematic procedure for guiding the task of building an intelligent trading system and (iii) a discussion about the main challenges and open problems in this scientific field. We examine the problem of discrete stock price prediction using a synthesis of linguistic, financial and statistical techniques to create the Arizona Financial Text System (AZFinText). The research within this paper seeks to contribute to the AZFinText system by comparing AZFinText's predictions against existing quantitative funds and human stock pricing experts. We approach this line of research using textual representation and statistical machine learning methods on financial news articles partitioned by similar industry and sector groupings. Through our research, we discovered that stocks partitioned by Sectors were most predictable in measures of Closeness, Mean Squared Error (MSE) score of 0.1954, predicted Directional Accuracy of 71.18 and a Simulated Trading return of 8.50 (compared to 5.62 for the S&P 500 index). In direct comparisons to existing market experts and quantitative mutual funds, our system's trading return of 8.50 outperformed well-known trading experts. Our system also performed well against the top 10 quantitative mutual funds of 2005, where our system would have placed fifth. When comparing AZFinText against only those quantitative funds that monitor the same securities, AZFinText had a 2 higher return than the best performing quant fund.