An algorithmic approach to handle circular trading in commercial taxing system

Tax manipulation comes in a variety of forms with different motivations and of varying complexities. In this paper, we deal with a specific technique used by tax-evaders known as circular trading. In particular, we define algorithms for the detection and analysis of circular trade. To achieve this, we have modelled the whole system as a directed graph with the actors being vertices and the transactions among them as directed edges. We illustrate the results obtained after running the proposed algorithm on the commercial tax dataset of the government of Telangana, India, which contains the transaction details of a set of participants involved in a known circular trade

A Graph Theoretical Approach for Identifying Fraudulent Transactions in Circular Trading

Circular trading is an infamous technique used by tax evaders to confuse tax enforcement officers from detecting suspicious transactions. Dealers using this technique superimpose suspicious transactions by several illegitimate sales transactions in a circular manner. In this paper, we address this problem by developing an algorithm that detects circular trading and removes the illegitimate cycles to uncover the suspicious transactions. We formulate the problem as finding and then deleting specific type of cycles in a directed edge-labeled multigraph. We run this algorithm on the commercial tax data set provided by the government of Telangana, India, and discovered several suspicious transactions

Simulation and Optimization of CNC controlled grinding processes : Analysis and simulation of automated robot finshing process

Products with complicated shapes require superior surface finish to perform the intended function. Despite significant developments in technology, finishing operations are still performed semi automatically/manually, relying on the skills of the machinist. The pressure to produce products at the best quality in the shortest lead time has made it highly inconvenient to depend on traditional methods. Thus, there is a rising need for automation which has become a resource to remain competitive in the manufacturing industry. Diminishing return of trading quality over time in finishing operations signifies the importance of having a pre-determined trajectory (tool path) that produces an optimum surface in the least possible machining time. Tool path optimization for finishing process considering tool kinematics is of relatively low importance in the present scenario. The available automation in grinding processes encompass around the dynamics of machining. In this paper we provide an overview of optimizing the tool path using evolutionary algorithms, considering the significance of process dynamics and kinematics. Process efficiency of the generated tool movements are studied based on the evaluation of relative importance of the finishing parameters. Surface quality is analysed using MATLAB and optimization is performed on account of peak to valley height. Surface removal characteristics are analysed based on process variables that have the most likely impact on surface finish. The research results indicated that tool path is the most significant parameter determining the surface quality of a finishing operation. The inter-dependency of parameters were also studied using Taguchi design of experiments. Possible combinations of various tool paths and tool influencing parameters are presented to realize a surface that exhibits lowest errors.European Horizon 2020 Project SYMPLEXIT

Big Data Analytics for Nabbing Fraudulent Transactions in Taxation System

This paper explains an application of big data analytics to detect illegitimate transactions performed by fraudulent communities of people who are engaged in a notorious tax evasion practice called circular trading. We designed and implemented this technique for the commercial taxes department, government of Telangana, India. This problem is solved in two steps. In step one, the problem is formulated as detecting fraudulent communities in a social network, where the vertices correspond to dealers and edges correspond to sales transactions. In step two, specific type of cycles are removed from each fraudulent community, which were identified in step one, to detect the illegitimate transactions. We used RHadoop framework for implementing this technique

Curtailing the Tax Leakages by Nabbing Return Defaulters in Taxation System

Tax evasion is an illegal activity where a taxpayer avoids paying his/her tax liability. Any taxpayer has to file their tax return statements periodically at regular intervals. Avoiding to file or delaying the filing of the tax return statement is one among the most basic methods of tax evasion. The taxpayers who are not filing returns or delaying the filing of returns are called return defaulters. Financial loss to the Government due to avoiding to file or delayed filing of returns varies between taxpayers. While designing any statistical model to predict return defaulters, we have to take into account the real financial loss associated with the misclassification. In this paper, we constructed an example dependent cost - sensitive logistic regression model that predicts whether a taxpayer is a potential return defaulter for the upcoming tax-filing period. While designing the model, we studied the effect of business interactions among the taxpayers on return filing behavior. We developed this model for the commercial taxes department, Government of Telangana, India. Applying our method to tax data, we show significant cost saving

Regression Analysis towards Estimating Tax Evasion in Goods and Services Tax

Tax evasion is as old as tax itself. In this paper, we devise a technique to predict the amount of tax-revenue lost by the state due to unscrupulous actions from a particular set of suspicious dealers. For the same, we build a regression model using the tax-return information of genuine business dealers and predict the amount of tax evaded by suspicious business dealers. Dealers are classified as genuine or suspicious by applying Benford's analysis on the different group of dealers formed after running k-medoids clustering algorithm over a set of dealers. In addition to getting an estimate on the loss of tax-revenue, results obtained from this work aid the tax enforcement officers on taking precautionary measures against tax evasion. The dataset used in the work is provided by the commercial tax department of Telangana state, India

Big Data Analytics for Tax Administration

The problem of tax evasion is as old as taxes itself. Tax evasion causes several problems that affects the growth of a nation. In this paper, we present our work in controlling tax evasion by using big data analytics, Android applications, and information technology. We implemented this work for the commercial taxes department, government of Telangana, India. Here we developed a complete software framework for scrutiny of suspicious accounts. This system detects suspicious dealers using certain sensitive parameters and standardizes the process of scrutiny of accounts. We used sophisticated statistical and machine learning tools to predict suspicious dealers. To increase the compliance levels, we developed a regression model for identifying return defaulters and user-friendly Android applications to assist the officers in collecting the tax. The other aspect we explored is the detection and analysis of a tax evasion mechanism, known as circular trading, using advanced algorithmic and social-network analytic techniques

Predictive Modeling for Identifying Return Defaulters in Goods and Services Tax

Tax evasion is an illegal practice where a person or a business entity intentionally avoids paying his/her true tax liability. Any business entity is required by the law to file their tax return statements following a periodical schedule. Avoiding to file the tax return statement is one among the most rudimentary forms of tax evasion. The dealers committing tax evasion in such a way are called return defaulters. In this paper, we construct a logistic regression model that predicts with high accuracy whether a business entity is a potential return defaulter for the upcoming tax-filing period. For the same, we analyzed the effect of the amount of sales/purchases transactions among the business entities (dealers) and the mean absolute deviation (MAD) value of the first digit Benford's law on sales transactions by a business entity. We developed this model for the commercial taxes department, government of Telangana, India

Clustering Collusive Dealers in Commercial Taxation System

Tax evasion is committed in a number of ways, in which, some are easily identifiable while few others are very difficult to detect. This article deals with a sophisticated technique used by tax-evaders known as circular trading. Dealers who commit this fraud often collude together and make bogus companies using fraudulent identities with the motivation to show heavy sales transaction among them. This huge quantity of data from transactions helps the dealers to hide their actual tax manipulation. Here, we devise clustering techniques that detects and groups together the dealers who are highly susceptible in performing circular trading. We represented the entire sales database for these dealers using weighted directed graphs. The clustering algorithm is run on the commercial tax dataset provided by the state government of Telangana, India, which helped in identifying potential circular trading activities