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Journal Article

Predictive Analytics in the Public Sector: Using Data Mining to Assist Better Target Selection for Audit  pp132-140

Duncan Cleary

© Dec 2011 Volume 9 Issue 2, ECEG, Editor: Frank Bannister, pp93 - 222

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Revenue, the Irish Tax and Customs Authority, has been developing the use of data mining techniques as part of a process of putting analytics at the core of its business processes. Recent data mining projects, which have been piloted successfully, have de veloped predictive models to assist in the better targeting of taxpayers for possible non‑compliance/ tax evasion, and liquidation. The models aim, for example, to predict the likelihood of a case yielding in the event of an intervention, such as an audit . Evaluation cases have been worked in the field and the hit rate was approximately 75%. In addition, all audits completed by Revenue in the year after the models had been created were assessed using the model probability to yield score, and a significant correlation exists between the expected and actual outcome of the audits. The models are now being developed further, and are in full production in 2011. Critical factors for model success include rigorous statistical analyses, good data quality, softwar e, teamwork, timing, resources and consistent case profiling/ treatments. The models are developed using SAS Enterprise Miner and SAS Enterprise Guide. This work is a good example of the applicability of tools developed for one purpose (e.g. Credit Scori ng for Banking and Insurance) having multiple other potential applications. This paper shows how the application of advanced analytics can add value to the work of Tax and Customs authorities, by leveraging existing data in a robust and flexible way to r educe costs by better targeting cases for interventions. Analytics can thus greatly support the business to make better‑informed decisions.


Keywords: tax, predictive analytics, data mining, public sector, Ireland


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Journal Article

A Roadmap for Analytics in Taxpayer Supervision  pp19-32

Mark Pijnenburg, Wojtek Kowalczyk, Lisette van der Hel-van Dijk

© Feb 2017 Volume 15 Issue 1, Editor: Mitja Dečman and Tina Jukić, pp1 - 56

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Tax administrations need to become more efficient due to a growing workload, higher demands from citizens, and, in many countries, staff reduction and budget cuts. The novel field of analytics has achieved successes in improving efficiencies in areas such as banking, insurance and retail. Analytics, which is often described as an extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact‑based management to drive decisions and actions (Davenport and Harris, 2007: 7), fits well in tax administrations, that typically have access to large volumes of data. In this paper we will answer the question how analytics contributes to a Compliance Risk Management approach – a major trend in taxpayer supervision in the last decade. The main tasks within compliance risk management include risk identification, risk analysis, prioritization, treatment, and evaluation. The answer of the research question gives more insight in what we can expect from analytics, and will assist tax administrations that want to improve their analytical capabilities. Attention is paid as well to limitations of analytics. Findings include that over half of the activities in taxpayer supervision can be supported by analytics. Additionally, a match is presented between supervision activities and specific analytical techniques that can be applied for these activities. The article also contains a short case study of the Netherlands Tax and Customs Administration on selection of VAT refunds with analytical techniques.


Keywords: tax administration, taxpayer supervision, compliance risk management, analytics and data mining


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Journal Issue

Volume 15 Issue 1 / Feb 2017  pp1‑56

Editor: Mitja Dečman, Tina Jukić

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Guest Editors

Decman‑Mitja‑EG‑031 Mitja Dečman is an Assistant Professor at the Faculty of Administration, University of Ljubljana, teaching undergraduate and postgraduate level. He holds a Ph. D. in Administration Science and a MSc. in Computer Science. His project and research work includes development of information systems, benchmarking systems, digital preservation, information security, e‑government, e‑governance, web 2.0 and others.



Tina Jukić is an Assistant Professor in the field of informatics in public administration at Faculty of Administration, University of Ljubljana, Slovenia. She gained her PhD in administrative science in 2013. In recent years her research activities are mainly focused on methodologies for the evaluation of e‑government projects and on social media usage in public administration. 


Keywords: social networking sites, public administration, level of usage, type of usage, engagement, literature review, tax administration, taxpayer supervision, compliance risk management, analytics, data mining, Conceptual data modeling, Object Role Modeling, Manual service, Digital Service, DPSIR, Zanzibar, information sharing arrangement, inter-organisational system (IOS), XBRL, standard business reporting, B2G, TOE


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