Segmentation of Potential Fraud Taxpayers and Characterization in Personal Income Tax Using Data Mining Techniques

This paper proposes an analytical framework that combines dimension reduction and data mining techniques to obtain a sample segmentation according to potential fraud probability. In this regard, the purpose of this study is twofold. Firstly, it attempts to determine tax benefits that are more likely to be used by potential fraud taxpayers by means of investigating the Personal Income Tax structure. Secondly, it aims at characterizing through socioeconomic variables the segment profiles of potential fraud taxpayer to offer an audit selection strategy for improving tax compliance and improve tax design. An application to the annual Spanish Personal Income Tax sample designed by the Institute for Fiscal Studies is provided. Results obtained confirm that the combination of data mining techniques proposed offers valuable information to contribute to the study of tax frau

Auditees case-selection model for evaluating taxpayer corporate tax compliance in Kenya

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityTax compliance rate in Kenya is estimated to be approximately below 65%. It is important for the government to place measures that ensure improved tax compliance rate comparable with benchmark countries like Sweden, whose tax compliance rate stand at 93%. One measure implemented in Kenya Revenue Authority has been to conduct scrutiny assessments on the taxpayer fraternity. However, success in scrutiny assessments in addressing payment and reporting compliance is largely dependent on the cases selected for audit. A major challenge has been in the possibility of selecting of an honest taxpayer and failure to take up the potential under-reporter, scenarios which are both costly to the tax administration. Whereas the honest taxpayer will feel unfairly selected for scrutiny, under-reporters escape the purview of the authority. This study presents a data mining based approach aimed at addressing the case-selection challenge. A classification model built using historical taxpayer audit data and decision tree algorithm was used to predict the compliance status of taxpayers in a case-selection application prototype. Experimental results using limited taxpayer data for the period year 2014/2015 indicate that the model is effective and fit for case-selection with an accuracy rate of 65% and prediction efficiency of 65% in identifying non-compliant taxpayers. Moreover, with more sources of taxpayer information and increased quantity of data, the accuracy and prediction efficiency is expected to improve significantly. It is recommended that Kenya Revenue Authority adopts this approach to improve the traditional case-selection by auditors‟ for corporate tax as well as other tax obligations such as Individual income tax, VAT, and custom duties administered by the Kenyan government

Combining Network Visualization and Data Mining for Tax Risk Assessment

This paper presents a novel approach, called MALDIVE, to support tax administrations in the tax risk assessment for discovering tax evasion and tax avoidance. MALDIVE relies on a network model describing several kinds of relationships among taxpayers. Our approach suitably combines various data mining and visual analytics methods to support public officers in identifying risky taxpayers. MALDIVE consists of a 4-step pipeline: ( ${i}$ ) A social network is built from the taxpayers data and several features of this network are extracted by computing both classical social network indexes and domain-specific indexes; ( ii ) an initial set of risky taxpayers is identified by applying machine learning algorithms; ( iii ) the set of risky taxpayers is possibly enlarged by means of an information diffusion strategy and the output is shown to the analyst through a network visualization system; ( iv ) a visual inspection of the network is performed by the analyst in order to validate and refine the set of risky taxpayers. We discuss the effectiveness of the MALDIVE approach through both quantitative analyses and case studies performed on real data in collaboration with the Italian Revenue Agency

Revealed corruption and electoral accountability in Brazil: How politicians anticipate voting behavior

Governments, civil society organizations, and scholars spend considerable resources implementing and evaluating the effect of anti-corruption interventions. However, decades of cumulative evidence suggest that these interventions rarely lead to the removal of corrupt elected officials from their positions. A recent interpretation of this gap suggests that corrupt politicians often go unpunished because they react to the knowledge of themselves or others being investigated for corruption in unanticipated ways. This dissertation uses data from a long-running anti-corruption program in Brazil to expand on the unintended consequences of anti-corruption interventions that stem from politicians' strategic behavior. The first chapter shows that mayors randomly selected for auditing in the context of this program reduce public spending, particularly in highly visible budget categories, in years close to an election. I argue that this happens because mayors attempt to preserve their reelection chances by signaling fiscal responsibility. The second chapter shows how mayors that are not directly audited, but are in municipalities close to those with mayors exposed as corrupt, tend to seek reelection under different parties more often. As previous accounts of party switching in Brazil suggest, I argue that this occurs because incumbent politicians expect their constituency to react to the news of nearby corruption with increased scrutiny on their own performance in office, which in turn leads them to switch parties in an attempt to secure a better platform for reelection. The question of the effect of exposure to information about nearby corruption opens the door to a broader methodological question of how to capture this type of effect, which is the focus of the third chapter. Research questions in the social sciences usually suggest spillover or interference effects, but rarely provide guidelines on how to model those effects. In fact, theory often suggests many different plausible operationalizations along the same hypothetical pathway. To overcome this difficulty, I propose and illustrate the properties of a model selection approach that uses tools from supervised machine learning to select among alternative operationalizations. As a whole, this dissertation makes two key contributions. First, it shows how politicians' reaction to anti-corruption interventions can stem from an attempt to avoid electoral accountability. Second, by proposing a model selection approach to interference, it expands the applicability of current tools to analyze interference effects to a broader set of research questions

DETERMINANTS OF LOAN AGREEMENT IN ASIA-PACIFIC

This study aims to investigates and analyze the interdependencies of three main variables of loan agreement. The three main variables are: collateral, maturity, and loan spread. This research is applied in Asia-Pacific corporate area between 2006 and 2010. This study used two stage least square regression analysis. This research used 6 models to describe the interdependencies of collateral, maturity, and loan spread to determine the loan agreement. This study used secondary data in the Dealscan database with 548 samples of Asia-Pacific corporates in 2006-2010. This study shows interdependencies of collateral, maturity, and loan spread. This research reveals that the main variable which affects the loan agreement consideration is collateral

Accountants\u27 index. Twenty-fourth supplement, January-December 1975

https://egrove.olemiss.edu/aicpa_accind/1026/thumbnail.jp

Accountants\u27 index. Twenty-first supplement, a bibliography of accounting literature, January 1972-December 1972 (Inclusive)

https://egrove.olemiss.edu/aicpa_accind/1023/thumbnail.jp

Accountants\u27 index. Twenty-seventh supplement, January-December 1978, volume 1: A-L

https://egrove.olemiss.edu/aicpa_accind/1031/thumbnail.jp