The recent introduction of new tax relief, Structure and Buildings Allowances, has widened the scope of qualifying expenditure and thus expanded the market to include many more companies who otherwise would not have claimed. The project between Veritas and Brunel University London will develop AI-assisted technology to aid SMEs to more efficiently collect and categorise data for tax assessment and tax relief.
There is currently a significant gap between the large corporates, who normally take advantage of the tax relief through expensive accounting consultancy, and SMEs who have been largely left out due to a lack of understanding of the regulations, and financial constraints. This project will create an affordable technical solution to enable the SMEs to capitalise on this opportunity.
Currently, data collection and categorisation are carried out through a manual process, which is a time-consuming and inefficient use of highly qualified consultants. The aim of this project is to develop an automated system for capital allowance assessment to collate and categorise the data required and to automatically generate tax claim reports that comply with the complex regulations.
The project will meet the following key objectives:
- to standardise the process of data entry to provide a complete and consistent collection of data;
- to develop a rule-based expert system to automate the process of capital allowance assessment;
- to support SMEs to claim the benefit of capital allowance and further their development and growth;
- to increase the Company's profits by increasing revenue from large corporates and growing their share of the SME market; and
- to engage with HM Revenue & Customs (HMRC) and the Royal Institution of Chartered Surveyors (RICS) to promote tax compliance on capital allowances.
Meet the Principal Investigator(s) for the project
Dr Yongmin Li
- Dr. Yongmin Li received his PhD from Queen Mary, University of London, MEng and BEng from Tsinghua University, China. Before joining Brunel University London, he worked as a research scientist in the British Telecom Laboratories. Dr. Li is a Senior Member of the IEEE, and Fellow of the Higher Education Academy. His research interest covers the areas of data science, machine learning, artificial intelligence, image processing, computer vision, video analysis, medical imaging, bio-imaging, biomedical engineering, healthcare technologies, automatic control and nonlinear filtering.Together with his colleagues, he has won the Most Influential Paper over the Decade Award at MVA 2019 and Best Paper Awards at Bioimaging 2018, HIS 2012, BMVC 2007, BMVC 2001 and RATFG 2001.
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