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AI-assisted tax assessment

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:

  1. to standardise the process of data entry to provide a complete and consistent collection of data;
  2. to develop a rule-based expert system to automate the process of capital allowance assessment;
  3. to support SMEs to claim the benefit of capital allowance and further their development and growth;
  4. to increase the Company's profits by increasing revenue from large corporates and growing their share of the SME market; and
  5. 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 - 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. PhD Studentships: We invite talented and hard-working students to join us for their PhD study. From time to time, we may have studentships available, which include an annual bursary (about £17,000 this year) plus payment of tuition fees for three years. Currently we have several projects on-going, for example, Deep Learning for Medical Imaging, Natural Language Processing for Tax Assessment, and Image/Video Content Generation for Personalised Remarketing. But any other topics within the area of artificial intelligence and data science would also be welcome.  Contact me for details if interested. Master of Science in Artificial Intelligence 2021/22: Built on our strong international research profile (consistently ranked in the top 100 in the world, 1st in UK for H-index and Highly Cited Papers for 3 years in a row from 2018-2020, and 3rd in UK for overall performance in "the NTU Performance Ranking of Scientific Papers for World Universities", Subject: Computer Science, 2020),  we offer the MSc Artificial Intelligence course with great flexibility (1 year full-time, 2 year part-time or 3 year staged study). If you are interested, apply here. 15 Scholarships available for applicants from under-represented groups, £10,000 of each.

Partnering with confidence

Organisations interested in our research can partner with us with confidence backed by an external and independent benchmark: The Knowledge Exchange Framework. Read more.

Project last modified 23/03/2021