The seminar was packed with panel attendees and delegates.

Panel Attendees were as follows:

• Jonathan Crook (Chair), Professor of Business Economics, Deputy Dean and Director of Research, University of Edinburgh Business School, Director of the Credit Research Centre
• Christian Burgin, CFA, Four Two Strategy Ltd
• Colin Garland, Director, Remedies, Business and Financial Analysis, Competition and Markets Authority
• Manuel Peleteiro, Founder, Inbestai

The conference addressed the Open Banking regulation, which went into effect in the United Kingdom in January 2018. The rule compels banks to allow third-party access to an account holder's banking data in an accessible format.

The seminar highlighted the fact that retail and business customers could now have access to new competitively priced products and services and the providers would be regulated by the Financial Conduct Authority (FCA) and European counterparts.

Unfortunately, this concept was not universally supported. Some anticipated that it would benefit just the technologically competent and exacerbate financial exclusion for low-income people. The question raised was whether it was reasonable to expect consumers to own their data and receive better deals from banks and other financial service providers and whether personal data revealed in places like social media could be misused. How could the bank be benefited from the regulation?

The successful applicant will work on research using transactions data for credit risk modelling with the ID Co. and be supervised by Jonathan Crook and Galina Andreeva.

Project Background

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.

On 5 November Jonathan Crook will be presenting in a session with Dan Kellett of Capital One on the validation of credit risk models at a conference called Validate AI at the Royal Society.

Jonathan will talk about topics including sources of model bias such as sampling bias, algorithmic bias, indirect bias, and the omission of important variables.

Find out more about the conference:

Validate AI Conference 2019

Jonathan Crook writes, “It’s an exciting time to be working at the crossroads of finance and technology, helping businesses improve their customer journeys and making the most of new sources of data.”

The full article can be read on the Business School website:

Read Setting the Credit Research Agenda

This opportunity is no longer available.

We are seeking to appoint two postdoctoral research fellows for a 6-month full-time fixed-term period (July–December 2019) to work on an exciting project with SAS and Nationwide.

The successful candidates should have a background in machine learning techniques and credit scoring models.

Informal enquiries about the post may be made to Raffaella Calabrese and Vaishak Belle.

This opportunity is no longer available.

The Business School has an opportunity for an individual with a background in mathematical data science or credit risk modelling to work on an EPSRC-funded project, ‘Optimisation Models for Interpretable Analytics’.

Full details can be found on the University of Edinburgh vacancies website under vacancy reference 047932.

As part of the CRC seminar series, Harald Scheule, currently Professor of Finance at the University of Technology Sydney, visited the Business School to present his recent research to staff, students, and practitioners. This research looked at using transactional (customer payments) data in mortgage risk modelling.

The research analysed the informativeness of non-mortgage bank payment transactions on mortgage default for a major retail bank, which found short-term interest coverage, income changes, home maintenance expenses, and cash withdrawals are strong predictors of future default. The use of transaction data improves model prediction allowing for earlier intervention that can assist consumers and lower bank losses.

Guest editors

Jonathan Crook, Christophe Mues, Tony Bellotti, and Galina Andreeva

Call for papers

Empirical and theoretical developments in credit risk modelling are proceeding at a faster pace than ever before. Alternative variables, new algorithms, new regulatory issues, new types of data and much larger volumes of data are all the subjects of the increasing number of papers in the literature. This special issue of JORS aims to bring together outstanding research in any area of credit risk modelling. Papers are invited on any aspect of credit risk modelling including (but not limited to) the following:

Papers may relate to credit given to any sector of an economy and any type of credit.

All papers will be subject to the usual refereeing process of JORS.

Please Note: Papers presented at the Credit Scoring and Credit Control XVI conference during 27-30 August 2019 at The University of Edinburgh may be submitted. However this is an open call and any papers in the area may be submitted even if they are not presented at the conference.

The Guest editors of this special issue will be: Professor Jonathan Crook (University of Edinburgh), Professor Christophe Mues (University of Southampton), Dr Tony Bellotti (Imperial College) and Dr Galina Andreeva (University of Edinburgh). The closing date for submissions is 17 January 2020 and submission should be made through the JORS website. All papers must comply with the submission requirements of JORS.

JORS Website

Angela De Moraes successfully defended her thesis on the topic of ‘Novel Information in Estimating Loss Given Default in Brazil’, whilst Denys Osipenko successfully defended his thesis on the topic of ‘An investigation into methods of predicting income from credit card holders using panel data’.

Very well done to both!

As part of his visit, he gave a presentation on ‘Market Power, State Intervention and Bank Risk during the Global Financial Crisis in the Euro Area’. The seminar was well attended by practitioners, faculty and students.

A former Lecturer in Business Economics at the Business School, Davide is now a Research Economist at the World Bank’s Development Research Group leading the update of the World Bank – Bank Regulation and Supervision survey. His main research interests lie in banking, focusing on bankruptcy prediction, credit risk, completion and efficiency.

More on Davide's Presentation