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PhD in Financial Technology Opportunity with IDCo.

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  • Full or part time
    Prof Jonathan Crook
    Dr Galina Andreeva
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (Students Worldwide)
    Competition Funded PhD Project (Students Worldwide)

Project Description

The traditional credit scoring models have used application form (and behavioural) variables with credit reference agency variables giving additional information on accounts at other lenders. However these predictors are, at the most frequent, measured monthly, the application variables (for example income, address etc.) are not updated and, crucially, they do not give an accurate direct indication of the ability of the account holder to repay any loans granted. Essentially these variables do not give an indication of an account holder’s cash flow.

On the other hand, account level transactions data provide daily information on all receipts and expenditures for an account holder for each account for which data is obtained. This information allows a very accurate daily measure of income (stable and volatile) and a fine classification of expenditures by service/product type and by merchant. Following expenditure categorisation and income aggregation across sources and classification into stable and volatile components, a full cash flow analysis for each account holder may be obtained on a daily basis. When used as covariates in a probability of default (PD) model, such covariates are expected to provide a much more accurate prediction of PD for each account holder than current models.

This project will develop a methodology for incorporating a novel type of digital data – financial transactions – into credit risk and affordability models. Transactional data provides a more accurate and up-to-date information about the financial status and behaviour of the borrower, compared to traditional data, which is static and often out-dated. Despite a great potential of transactional data, it is current use is limited because of technical problems which this project will overcome. The project will experiment with innovative categorisation/ aggregation algorithms. It will also estimate application and behavioural credit risk models using a range of advanced statistical and machine-learning algorithms.


ABOUT OUR SCHOOL
Based in the central campus at the University of Edinburgh, the Business School seeks to set the agenda across a wide range of business disciplines, with our research areas staffed by teams whose work has real influence in business, government, and the public sector.

The University of Edinburgh was ranked as 4th in the UK for research power (Times Higher Education, 2019), and the Business School has significant research expertise across six subject areas. Our largest subject area, Management Science and Business Economics , where this project resides, includes influential subject-matter experts such as Prof Jonathan Crook, who will supervise this project alongside Dr Galina Andreeva.

We aim to provide a highly dynamic and supportive research environment for our large and diverse graduate community, ensuring that Business School PhD students have the resources required to make the most of their individual projects. This is evidenced by our positive student feedback in national surveys such as the PRES 2019.

In addition, the Business School has received full research training accreditation both from the Economic and Social Research Council (ESRC) and European Quality Improvement System (EQUIS), and holds the Athena SWAN Bronze Award in recognition of the positive work the School has done to promote diversity and gender equality.


HOW TO APPLY
Applicants must meet the following entry PhD in Financial Technology requirements for this project:

1. The academic entry requirement.
This normally requires a minimum qualification (or expected qualification if you are current Masters’ student) of above-average academic achievement, quantified as a 70% or above overall at the Masters level, with a distinction level dissertation (or UK equivalent) in (for this project) the subjects of: Statistics or Informatics or Econometrics. Students with significant finance and technology industry experience, or with relevant professional qualifications, that also have a minimum of a Bachelor’s degree in the programmes stated above will be given due consideration on a case-by-case basis.

2. The English requirement.
Full details of the English requirements can be found on our School website here: https://www.business-school.ed.ac.uk/phd/entry-requirements. The most commonly approved certificate is an IELTS, for which the minimum accepted score is 7.0 overall with at least 6.0 in each section.

To apply applicants should send their current academic CV, a copy of your Masters’ level transcript (or interim transcript if you are completing your studies) and a cover letter describing why you would be suitable for the project. Eligible applicants will be ranked by a selection panel and applicants will be notified if they have been shortlisted for interview.

Interviews will be scheduled during the week commencing 11th November.

Funding Notes

This opportunity is linked directly to a joint EIT Digital and The IDCo sponsored project. Therefore, the successful candidate will be expected to achieve the project under the supervision of Professor Jonathan Crook and Dr Galina Andreeva, as well as the team at The IDCo.

The project is fully funded, covering tuition fees and an enhanced stipend for up to four years.

Please note that all awards are subject to candidates successfully securing admission to the PhD in Financial Technology programme within the University of Edinburgh.

How good is research at University of Edinburgh in Business and Management Studies?

FTE Category A staff submitted: 51.60

Research output data provided by the Research Excellence Framework (REF)

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