This scholarship is linked to a joint EIT Digital and the ID Co. sponsored project. The successful candidate will be expected to undertake the project under the supervision of Professor Jonathan Crook, Dr Galina Andreeva, and the team at the ID Co.
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Full tuition fee coverage and £20,000 stipend awarded annually over a four-year period
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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, and so on) 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 provides 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 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about the financial status and behaviour of the borrower, compared to traditional data, which is static and often outdated. Despite the great potential of transactional data, its 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.
Applicants must meet the following entry requirements for the PhD in Financial Technology.
- Normally requires a minimum qualification (or expected qualification if you are a current Master's student) of above-average academic achievement, quantified as 70% or above overall at master's level. For this project you will require a distinction-level dissertation (or UK equivalent) in statistics, informatics, or econometrics.
- Students with significant finance and tech industry experience, or with relevant professional qualifications and a minimum of a bachelor's degree in the programmes stated above will be given due consideration on a case-by-case basis.
English Language Requirement
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.
How to Apply
If you are interested in applying for this project, please email email@example.com to discuss it further.
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 11 November.
All scholarship awards are subject to candidates successfully securing admission to the PhD in Financial Technology programme at the University of Edinburgh.
Professor Jonathan Crook
+44 (0)131 650 3802
Dr Galina Andreeva
+44 (0)131 651 3293
Postgraduate Research Support and Development Officer
+44 (0)131 651 5337
University of Edinburgh Business School
29 Buccleuch Place