phd in credit risk

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Opportunities for doctoral research at the Credit Research Centre

The Credit Research Research Centre (CRC) is one of the UK’s leading centres for research into all aspects of credit to both consumers and customers. Members of the CRC have research interests ranging broadly across the economics of credit, credit scoring, customer scoring and SME risk modelling. Credit scoring involves the application of models to predict the probability that an applicant for credit will default on the loan. It has been described as the most successful application of Operational Research techniques ever. Members of the Centre have written the world’s most authoritative text on the subject as well as having published in the world’s best journals in the area.

Following is a sample of the range of opportunities for doctoral study which currently exist within the Centre.

Natural Language Processing and Multimodal Learning for Financial Risk and Default Prediction

Recent advancements in Natural Language Processing (NLP) and multimodal learning have unlocked new opportunities to enhance financial performance and risk prediction. Techniques such as transformer models and BERT have excelled in extracting valuable insights from textual data. Moreover, integrating multimodal data—including text, numerical data, voice, and visual information—has shown great promise in improving predictive models. These innovations underscore the significance of interdisciplinary approaches to tackle complex financial challenges. We invite motivated PhD candidates to contribute to cutting-edge projects in this dynamic field.

Required Skills and Qualifications

Educational background.

• A strong first degree in Quantitative Finance, Business Analytics, Data Science, Computer Science, Statistics, or a related field.

Machine Learning Knowledge

• Solid foundation in machine learning algorithms and their practical applications
• Familiarity with multimodal learning and data integration techniques is a plus.

NLP Expertise

• Comprehensive understanding of natural language processing techniques and tools
• Experience with transformer models, BERT, GPT, or similar frameworks is highly desirable.

Programming Skills

• Proficiency in programming languages such as Python or R
• Experience with machine learning libraries is essential.

Supervisor: Dr Yizhe Dong

Ethical Artificial Intelligence models

The adoption of Artificial Intelligence (AI) technologies in the Banking industry has boosted over the last half a decade. A debate has emerged on different ethical concerns and consequences of the use of AI, not only in banking, but also other areas such as justice, law enforcement and medicine. This project will explore the development of AI-based methodologies that can overcome or mitigate these issues, focusing on applications to credit scoring and financial risk management.

Required qualifications: A good first degree in Business Analytics, Operational Research, Data Science, Computer Science, or Engineering.

Supervisor: Dr Belen Martin-Barragan

Further information

If you have any questions about these topics, please contact Credit Research Centre .

Opportunities for Doctoral Research at the Credit Research Centre

The Credit Research Research Centre (CRC) is one of the UK’s leading centres for research into all aspects of credit to both consumers and customers. Members of the CRC have research interests ranging broadly across the economics of credit, credit scoring, customer scoring and SME risk modelling. Credit scoring involves the application of models to predict the probability that an applicant for credit will default on the loan. It has been described as the most successful application of Operational Research techniques ever. Members of the Centre have written the world’s most authoritative text on the subject as well as having published in the world’s best journals in the area.

Following is a sample of the range of opportunities for doctoral study which currently exist within the Centre.

Stress Testing Consumer Credit Portfolios

Regulators and financial, institutions have been ‘stress testing’ their loan portfolios to examine the amount of capital that an institution may expect to maintain to cover for unexpected losses. However there are many different types of stress test and many methodological difficulties in carrying a stress test out. In this project the student will consider how discrete survival models can be used to enable stress tests to be carried out in particular how the macreoeconomic variables can be ‘combined’ in some sense to yield a valid ‘test’. The stability of expected loss distributions will be considered in detail.

Contact: Professor Jonathan Crook

Modelling Loss Given Default and Recoveries for Corporate Loans

Each bank that is regulated under the Basel III Accord has to maintain an amount of capital in case of unexpected losses. The Accord indicates how the minimum amount of capital should be computed. The amount depends on the amount of risk weighted assets that a bank has and an important component of this is the forecast levels of Loss Given Default for each bucket of a loan portfolio. In this project the LDG for each of a large sample of corporate loans will be modelled. Various algorithms and data transformations will be considered as will the incorporation of macroeconomic variables. Stress testing will be carried out.

Risk Concentration

The amount of capital a bank is required to hold in case of unexpected losses is governed by the Basel II Accord. The relevant computation of the risk weighted assets makes some ‘assumptions’ about the correlation between the asset values. The aim of this project is to explore methods of measuring the degree of concentration of risk in a credit portfolio. The project will consider ad hoc and model based measures and practical approaches to its empirical measurement.

Modelling Bankruptcies

There is a large amount of literature on reasons for person bankruptcy. In this project the sociodemographic characteristics of those who declare bankruptcy will be modelled using mixed fixed and random effect models. The fixed effects will include states of the macroeconomy. Initially publicly available data from surveys will be used. Later we will be to seek data from a financial institution.

Exploring Affordability in Retail Credit

Traditional credit scoring used by the high street lenders creates two classes those who will be given credit and those who will not. Individuals are therefore excluded from credit and often seek alternative and more expensive forms of credit.

More recently risk-based pricing has been introduced by high street lenders to provide credit to individuals who under traditional models would not have received credit but who will be charged a rate associated with the their risk profile. This means individuals with low risk ratings will be required to higher interest rates. This raises the question of whether individuals will be able to afford the credit and the need for the lender to be able to assess the individual’s ability to pay. The research will explore both of these related aspects. It will explore the available data on individual economic resources arising from Income and Expenditure Surveys, and other published sources. It will also explore how this information can be used to enhance the models currently used to assess affordability.

Comparisons of Household Credit Constraints

Credit constraints have profound implications for the applicability of the Permanent Income Hypothesis, for the effects of certain government fiscal and credit policies. However the empirical literature has not definitively identified which households are credit constrained and how the characteristics of those constrained has changed before and after the recent financial crisis. The aim of this project is to identify the characteristics of households who are credit constrained over the last decade in a variety of countries using different data sources. These include self-reported credit constraints, implied credit constraints of ten used in the excess sensitivity of consumption literature, demand and supply of debt equations and administrative data.

Required qualifications: a good first degree in Economics and preferably an MSc in Economics.

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Credit Risk

In this book, two of America’s leading economists provide the first integrated treatment of the conceptual, practical, and empirical foundations for credit risk pricing and risk measurement. Masterfully applying theory to practice, Darrell Duffie and Kenneth Singleton model credit risk for the purpose of measuring portfolio risk and pricing defaultable bonds, credit derivatives, and other securities exposed to credit risk. The methodological rigor, scope, and sophistication of their state-of-the-art account is unparalleled, and its singularly in-depth treatment of pricing and credit derivatives further illuminates a problem that has drawn much attention in an era when financial institutions the world over are revising their credit management strategies.

Duffie and Singleton offer critical assessments of alternative approaches to credit-risk modeling, while highlighting the strengths and weaknesses of current practice. Their approach blends in-depth discussions of the conceptual foundations of modeling with extensive analyses of the empirical properties of such credit-related time series as default probabilities, recoveries, ratings transitions, and yield spreads. Both the “structura” and “reduced-form” approaches to pricing defaultable securities are presented, and their comparative fits to historical data are assessed. The authors also provide a comprehensive treatment of the pricing of credit derivatives, including credit swaps, collateralized debt obligations, credit guarantees, lines of credit, and spread options. Not least, they describe certain enhancements to current pricing and management practices that, they argue, will better position financial institutions for future changes in the financial markets.

Credit Risk  is an indispensable resource for risk managers, traders or regulators dealing with financial products with a significant credit risk component, as well as for academic researchers and students.

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Graduate Research

Doctoral Program in Decision, Risk and Financial Sciences

• Arrow-right #1 Business and Economics in Australia
• Arrow-right #8 Globally for graduate employability

Course overview

What is it about.

The Doctoral program in Decision, Risk and Financial Sciences at the University of Melbourne in the Department of Finance provides training in the conceptual principles and research techniques across the social, biological and mathematical sciences fields that analyse human decision-making and problem-solving in the context of risk and complexity at individual and market levels. Students will draw on experimental methods and conceptual principles to deliver solutions to key research questions relating to decision-making, risk and financial sciences.

The program is made up of a 2-year Master of Commerce coursework program followed by a 3-year PhD.

Generous scholarships are available for high achieving applicants, including full fee waivers and a stipend of AUD 37,000 per year (2024 RTP rate).

Research conference travel funding of AUD$15,000 is available to all confirmed PhD candidates.

Training is provided by leading researchers from the Faculty of Business and Economics, the Faculty of Medicine, Dentistry and Health Sciences, the Florey Institute of Neuroscience and Mental Health, and the Melbourne School of Engineering. It includes two lab rotations in labs at the University of Melbourne or elsewhere.

After the coursework phase, students will conduct their research projects in a lab at the University of Melbourne, such as the Brain, Mind and Markets Lab . Research projects can span multiple disciplines and labs.

The program is located in Parkville, one of the largest precincts for biomedical research in the world, which provides access to cutting-edge research facilities, including state-of-the-art biomedical imaging platforms.

The Faculty of Business and Economics also has excellent relationships with industry and government, and the program encourages students to conduct research in collaboration with private and public sector partners.

Register to attend an information session

Upcoming  Event

Related study areas

• Business and economics
• Computer science
• Economics, econometrics and analytics
• Information technology and computer science
• Psychological sciences

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• The equity gap

Equity finance for business growth

Experts in credit management research

Building strong relationships with the finance and credit industry and with policymakers

Accounting and Finance Department

Highly ranked across all three league tables (The Complete University Guide, The Guardian and The Times)

• Credit management research
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Credit Management Research Centre

The credit management research centre (cmrc).

The Credit Management Research Centre (CMRC) was established in 1998, with funding from the  Institute of Credit Management (now the Chartered Institute of Credit Management CICM) , commercial sponsors from the credit industry, and research grants.

Recognising the important role of knowledge transfer activities, the research programme combines academic rigour with practitioner and policy relevance. We consider industry and policy partnerships and engagement as critical factors in our commitment to dynamic and relevant research, innovative teaching, and engaging PhD and Research Degrees.

Our research

Our experts focus on three main areas of credit management research:

• Consumer credit
• Commercial credit
• Credit scoring, risk and propensity modelling

Celebrating the Credit Management Research Centre's anniversary

Funding to date

Commercial sponsorship, research grants and government projects

Best application of management or social science

2007 National Knowledge Transfer Partnership Awards

• Recent work with the Department for Business, Energy and Industrial Strategy (BEIS) has focussed on business finance and policy forecasting. This includes studies of equity finance provision in the UK and regions, and modelling the costs, risks and benefits of COVID loans schemes. Centre Director, Professor Nick Wilson, was a member of the rapid response policy team at BEIS during the pandemic period. The work on equity finance formed a Research Excellence Framework (REF) 2021 Impact Case .
• CMRC has had funding for Knowledge Transfer Partnerships (KTPs) with consumer credit providers. Recent work has explored the utility of Artificial Intelligence in consumer credit risk modelling and credit scoring with Novuma Consumer Finance .
• Professor Wilson is co-investigator on a project funded by the ESRC that is analysing productivity in the Small Firm sector and access to finance. The work is being coordinated across the Universitys of Leeds, Edinburgh, St Andrews, Bath, Warwick, Susses and Oxford Brookes.
• Professor Wilson was awarded a University of Leeds ‘Certificate of Outstanding Achievement’ for ‘Academic Excellence in Enterprise and Knowledge Transfer’ in 2006. In 2022 he received an award for Research Impact.
• The spin-out company, CreditScorer Ltd set up in 2001, provided risk and portfolio management solutions for lenders to the UK corporate sector. It was owned and controlled by the University of Leeds from inception in 2001 until it was acquired by Bisnode AB in 2007.
• CMRC colleagues have contributed research and analysis to inform policy decisions and implementation. Including work for BEIS, HM Treasury, Bank of England, HMRC, British Business Bank and the National Audit Office. CMRC continues to work with professional bodies and commercial organisations in the credit industry.
• Professor Wilson is currently acting as a judge for the 2023 British Credit Awards .

Credit management is established on the curriculum within the University of Leeds degree programmes, particularly in the area of risk modelling and credit scoring. These include our  BSc Accounting and Finance  and  MSc Accounting and Finance  programmes.

Doctoral research has been an integral part of the work carried out by the Centre and has achieved a number of PhDs in areas ranging from risk modelling, trade credit and working capital management to private equity and venture capital investment. CMRC invites applicants for PhDs in credit related research.  Further information on our PhD and Research Degrees can be found here .

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