- Value: This project is open to self-funded students worldwide.
UK and EU applicants are eligible for funding from the EPSRC NPIF scholarship.
- Number of awards: 1
- Deadline: 31 May 2018
Type of project
Competition funded PhD projects
Contact Dr Georgios Aivaliotis to discuss this project further informally.
HMRC collects a wealth of data regarding tax compliance of companies and individuals. Sometimes people and companies do not pay the correct amount of tax on time for a variety of reasons (e.g. lack of knowledge, lack of ability, evasion). The data collected are “big”, i.e. a high number of variables and many clients and are of both temporal (time stamped) as well as static nature.
The aim of this project will be to develop the necessary methodology that allows to extract information from the data and to apply machine learning and pattern mining alongside classical statistical techniques in order to predict which cases are most likely to result in non-compliance so that early action can be taken. Linking SME’s and HMRC data will be an additional possibility and challenge. As a follow-up, economic models will be developed that look into the cost of interventions and what actions are economically meaningful to ensure compliance.
The successful PhD candidate will work under the guidance of an academic as well as industrial (HMRC Digital Academy and Cambridge Spark) supervisor(s). HMRC and Cambridge Spark will provide expertise in the data, the possibility of working onsite and training. Cambridge Spark offers a variety of training, conferences and workshops in AI and data analytics methodology. HMRC Digital Academy runs a series of regular seminars and are investing in research in data analytics.
Applicants should have, or expect to obtain, a minimum of a UK upper second class honours degree in Mathematics or a related discipline, or equivalent. Applicants whose first language is not English must also meet the University’s English language requirements.
How to apply
Formal applications for research degree study should be made online through the university’s website. Please state clearly in the research information second that the PhD you wish to be considered for is ‘Predictive analytics for tax compliance’ as well as Dr Georgios Aivaliotis as your proposed supervisor.
If English is not your first language, you must provide evidence that you meet the University’s minimum English Language requirements.
We welcome scholarship applications from all suitably-qualified candidates, but UK black and minority ethnic (BME) researchers are currently under-represented in our Postgraduate Research community, and we would therefore particularly encourage applications from UK BME candidates. All scholarships will be awarded on the basis of merit.