In this PhD-thesis several new and existing data science application are described that are particularly focused on applications for tax administrations. The thesis contains a chapter on the... Show moreIn this PhD-thesis several new and existing data science application are described that are particularly focused on applications for tax administrations. The thesis contains a chapter on the managerial side of analytics with a balanced overview of the pros and cons of applying analytics within taxpayer supervision. Another topic is (tax) fraud detection with unsupervised anomaly detection techniques. Here a new type of outliers is described (singular outliers) and an algorithm is provided for finding them. Attention is also paid to improving risk selection models. It is noted that most current algorithms cannot treat interactions of categorical variables with many levels very well. An extension of logistic regression is provided that uses Factorization Machines, which resulted in a ten percent improvement in precision. A fourth topic is statistical testing on similar treatment of similar cases. A contribution is made by providing an algorithm to statistically test on similar treatment based on process logs. The thesis contains further a benchmark study of different anomaly detection algorithms. Finally HR Analytics, Reinforcement Learning and applications of fuzzy sets are shortly described. Show less
Pijnenburg, M.G.F.; Kowalczyk, W.J.; Hel-Van Dijk, E.C.J.M. van der 2017
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... Show moreTax 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 improvingefficiencies 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 andactions (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 majortrend in taxpayer supervision in the last decade. The main tasks within compliance risk management include riskidentification, risk analysis, prioritization, treatment, and evaluation. The answer of the research question gives moreinsight in what we can expect from analytics, and will assist tax administrations that want to improve their analyticalcapabilities. Attention is paid as well to limitations of analytics. Findings include that over half of the activities in taxpayersupervision can be supported by analytics. Additionally, a match is presented between supervision activities and specificanalytical techniques that can be applied for these activities. The article also contains a short case study of the NetherlandsTax and Customs Administration on selection of VAT refunds with analytical techniques. Show less
