TARC ESRC Legacy Status
Lead Research Organisation:
UNIVERSITY OF EXETER
Department Name: Economics
Abstract
During the Legacy Status period, the Research Centre will communicate its research findings and impact-led work to the widest possible audience of international tax researchers. Dissemination takes various forms, including through the organisation of workshops, conferences and specialised capacity building seminars and builds on the significant track record of international events organised by TARC which demonstrates that the Research Centre has developed the expertise required to continue to do so successfully. The Research Centre will also build additional capacity through engagement with researchers and policymakers from Revenue Authorities and Government bodies in developing countries, and particularly Africa. Assisting developing countries in their efforts to improve revenue mobilization and achieve sustainable development has been a key research and impact priority of the Centre. Through TARC's continuing programme of specialised seminars given the Revenue Authorities in developing countries, knowledge transfer is achieved and much-needed capacity building is enhanced. The Research Centre will also broaden its extensive research and policy network of International Fellows, and its successful active visitors programme. This proposed activity will contribute to maximising the international visibility of the Centre and to boosting global knowledge transfer, thereby enhancing TARC's reputation as an international hub where ideas are articulated and developed.
TARC has already built up an excellent network of stakeholders - including but not restricted to: the Tax Administration Diagnostic Assessment Tool (TADAT), International Monetary Fund; World Bank: OECD; Independent Authority of Public Revenue of the Hellenic Republic; Rwanda Revenue Authority; Bulgarian National Revenue Authority (BNRA): Welsh Government: African Tax Administration Forum (ATAF); Her Majesty's Revenue and Customs; the Directorate General of Taxation of Cameroon; Namibia Revenue Authority: the Digital Economy Taxation Foundation and the Vienna University of Economics and Business - and will be developing this engagement further in order to disseminate TARC's research and further policy engagement and impact. These collaborations have had research and policy impact. The collaboration with BNRA has contributed to significant revenue gain for Bulgaria and more efficient targeting of tax audits, to the extent that it generated an impact statement from the BNRA to the OECD which emphasise the impact: 'To combat VAT fraudulent transactions the Bulgarian National Revenue Agency (BNRA) for over ten years has been using a two-step process. This two-stage process discretely combines data analytics with operational knowledge. As such it is highly labour-intensive as requires input from the most experienced BNRA tax professionals In 2019 a co-operation was established between the BNRA and the Tax Administration Research Centre (University of Exeter Business School), and University College London. The main objective of this co-operation was to develop a scalable predictive model, which automatically identifies missing traders. A key aspect of the methodological approach developed is that the network structure of all VAT transactions (through sales/purchases invoices) provides important information which can (and should) be used to identify abnormal VAT transactions through the production chain. In early 2021, BNRA started implementing the predictive model, partially utilising the information structure emerging from the rich VAT transactions data collected. The first results have shown that such an approach can achieve both faster identification of fraudulent VAT transactions, but also a significantly higher proportion of identified real missing traders. This means the time a missing trader begins its fraudulent activities and the time it is identified by BNRA is on average reduced by 15%. Importantly, the predictive model can now identify fraudulent transactions
TARC has already built up an excellent network of stakeholders - including but not restricted to: the Tax Administration Diagnostic Assessment Tool (TADAT), International Monetary Fund; World Bank: OECD; Independent Authority of Public Revenue of the Hellenic Republic; Rwanda Revenue Authority; Bulgarian National Revenue Authority (BNRA): Welsh Government: African Tax Administration Forum (ATAF); Her Majesty's Revenue and Customs; the Directorate General of Taxation of Cameroon; Namibia Revenue Authority: the Digital Economy Taxation Foundation and the Vienna University of Economics and Business - and will be developing this engagement further in order to disseminate TARC's research and further policy engagement and impact. These collaborations have had research and policy impact. The collaboration with BNRA has contributed to significant revenue gain for Bulgaria and more efficient targeting of tax audits, to the extent that it generated an impact statement from the BNRA to the OECD which emphasise the impact: 'To combat VAT fraudulent transactions the Bulgarian National Revenue Agency (BNRA) for over ten years has been using a two-step process. This two-stage process discretely combines data analytics with operational knowledge. As such it is highly labour-intensive as requires input from the most experienced BNRA tax professionals In 2019 a co-operation was established between the BNRA and the Tax Administration Research Centre (University of Exeter Business School), and University College London. The main objective of this co-operation was to develop a scalable predictive model, which automatically identifies missing traders. A key aspect of the methodological approach developed is that the network structure of all VAT transactions (through sales/purchases invoices) provides important information which can (and should) be used to identify abnormal VAT transactions through the production chain. In early 2021, BNRA started implementing the predictive model, partially utilising the information structure emerging from the rich VAT transactions data collected. The first results have shown that such an approach can achieve both faster identification of fraudulent VAT transactions, but also a significantly higher proportion of identified real missing traders. This means the time a missing trader begins its fraudulent activities and the time it is identified by BNRA is on average reduced by 15%. Importantly, the predictive model can now identify fraudulent transactions
