PAYE anytime is a web application designed and implemented by the Office of the Revenue Commissioners of Ireland. The application allows the Pay As You Earn (PAYE) customers in Ireland to manage most of their tax affairs online. By using easily accessible technology, PAYE customers can update their information and process most of their tax credits and reliefs online in a clear and effective manner. This online system was designed and implemented in order to reduce the volume of direct contacts between Revenue and its PAYE customers, to decrease costs and improve overall efficiency and effectiveness within the organisation. Moreover, the usage of such an e‑channel allows Revenue to record important information that can be analysed with the aim of improving overall customer service. Therefore, management of this strategic contact channel is paramount to Revenue’s continued advancement and improvement of its online services. This paper describes a segmentation of PAYE anytime users. The segmentation was conducted to understand the profiles and behaviours of these customers. This unsupervised data mining method produces an unbiased, self directed portrait of PAYE anytime customers. The data analysed were extracted from the weblogs of the PAYE anytime online application, which contains information about the users’ navigation. The data were linked to the users’ information held in the Revenue data warehouse in order to access all recorded details about PAYE anytime users. This information consists of the tax credits claimed, the value of tax credits, time period and similar attributes. By linking the online behaviour with the users’ information and mapping on the demographic details of the users, it was possible to identify the different segments and their profiles. The results of this segmentation improve Revenues understanding of the PAYE customer base. Knowledge gained with this project can be applied in a number of areas. Naturally, the profiles and behaviours associated with each segment can be strategically used for customer intelligence policies, allowing specific services to be tailored around customer profiles. Moreover, the analysis can point to improvements of the design and structure of future iterations of the PAYE anytime application.

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.

In spite of the gradual increase in the number of academic studies on smuggling crime, focus is seldom placed on the application of data mining to crime prevention. This study provides deeper understanding and exploration of the benefits of information technology for the identification of smuggling crime. This study focuses on smuggling of vessels. The data source is the complete record of fishing vessels leaving and returning to ports in the Taiwan region. This paper essay applies both artificial neural networks (ANN) and logistics regression (LR) to classify and predict criminal behaviors in smuggling. At the same time, it shows the difference between ANN and human inspection (HI), also the difference between LR and HI. This study establishes models for vessels of different tonnage and operation purposes that can provide law enforcers with clearer judgment criteria. It is needed to construct different models for vessels to achieve the actual cases in the reality since smugglers will use different kinds of ship for different smuggling purposes. The study results show that the application of artificial neural networks to smuggling fishing vessels attains an average precision of 76.3%, and the application of logistic regression to smuggling fishing vessels can achieve an average precision of 60.5%, both of which are of significantly higher efficiency levels compared with the current human inspection (HI) method. This study suggests the value of using an artificial neural networks model to obtain good identification performance for different vessel types as well as average savings of 90.47% on the manpower loading. Information technology can greatly help to increase the probability of seizing smuggling fishing vessels. Nowadays, public administration information is saved electronically however is not employed well. In fact, it can increase the administrative efficiency by proper use of electronic data. In this study, for example, we expect better use of the data stored in the database to establish an identifying model of smuggling. Applying the automatic identification mechanism, it is useful to reduce the probability of smuggling crime.

This study proposes data mining solutions for local municipalities to make their decision support mechanism easier. The purpose of this study is to get intelligent solutions related to local government services from past data and to estimate the future activities. It covers socio‑cultural analyses, income/expense analyses, infrastructure analyses, fraud detection analyses, simplification, verification and similarity analyses. Proposed system is based on service oriented architecture. The purposes of this project are; to give information about current state, to facilitate decision making for future activities, to increase income and decrease expense, to supply easy and correct data input to the system and to supply easier document tracking system. Seventeen scenarios were created initially. These scenarios are; Staff Analyzing, Classifying Citizens According to Real Estate Tax, Distribution of Citizens delaying Real Estate Tax, Income Operations Analyzing, Fuel Oil Analyzing, Electricity Consumption Analyzing, Cash Desk Analyzing, Distribution of Corporate Foundation, Moveable Material Analyzing, Logs Analyzing, Water Notice Analyzing, User Accounts Analyzing, Accountancy Analyzing, Employee Analyzing, Estimation of Wages, Citizen Analyzing and Corporate Foundation Analyzing. Service Oriented Architecture (SOA) is used as software architecture. Five services ‑ Association Rule Mining Web Service (ARMWS), Outlier Detection Analysis Web Service (ODAWS), Classification Web Service (CWS), Clustering Web Service (ClustWS) and Data Preparation Web Service (DPWS) ‑ were created. 7 scenarios used ARMWS, 3 scenarios used ODAWS, 2 scenarios used CWS and ClustWS is used by 5 scenarios.

This study proposes data mining solutions for local municipalities to make their decision support mechanism easier. The purpose of this study is to get intelligent solutions related to local government services from past data and to estimate the future activities. It covers socio‑cultural analyses, income/expense analyses, infrastructure analyses, fraud detection analyses, simplification, verification and similarity analyses. Proposed system is based on service oriented architecture. The purposes of this project are; to give information about current state, to facilitate decision making for future activities, to increase income and decrease expense, to supply easy and correct data input to the system and to supply easier document tracking system. Seventeen scenarios were created initially. These scenarios are; Staff Analyzing, Classifying Citizens According to Real Estate Tax, Distribution of Citizens delaying Real Estate Tax, Income Operations Analyzing, Fuel Oil Analyzing, Electricity Consumption Analyzing, Cash Desk Analyzing, Distribution of Corporate Foundation, Moveable Material Analyzing, Logs Analyzing, Water Notice Analyzing, User Accounts Analyzing, Accountancy Analyzing, Employee Analyzing, Estimation of Wages, Citizen Analyzing and Corporate Foundation Analyzing. Service Oriented Architecture (SOA) is used as software architecture. Five services ‑ Association Rule Mining Web Service (ARMWS), Outlier Detection Analysis Web Service (ODAWS), Classification Web Service (CWS), Clustering Web Service (ClustWS) and Data Preparation Web Service (DPWS) ‑ were created. 7 scenarios used ARMWS, 3 scenarios used ODAWS, 2 scenarios used CWS and ClustWS is used by 5 scenarios.

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.