Agenda

Agenda

Credit Risk Modelling Agenda

VIRTUAL, ALL TIMES IN BST

14:00 – 15:00

Best practice credit risk modelling
9.00 am US / 2:00 pm UK

02:00 - 03:00

  • Example rating systems based on default data
  • Modelling for low-default portfolios
  • Modelling macroeconomic drivers for IFRS 9
  • Estimating rating migration matrices
  • Connecting credit risk portfolio and LGD modelling
Ioannis Tsakanikas

Credit Risk Modelling SME

Morgan Stanley

Ioannis has extensive experience in development and validation of credit risk models, including IRB, IFRS9 and stress testing models. He has worked and consulted for major banking groups, like HSBC, Santander and currently Morgan Stanley. Ioannis holds a PhD in Mathematics and is a CFA and PRM charterholder.  

15:00 – 15:15

Break
10:00 am US / 3:00 pm UK

15:00 - 15:15

15:15 – 16:15

AI and machine learning in credit risk modelling
9.00 am US / 2:00 pm UK

14:00 - 15:00

  • Overview of machine learning models used in credit
  • Challenges in applying ML to credit
  • Collaborative portfolios
  • Model interpretability and regulatory requirements
Jesús Calderón

Managing director

Maclear Data Solutions

Jesús Calderón advises Canadian and international clients in the financial services and energy industries on the implementation of data-driven solutions for risk management in  banking, insurance, capital markets, and energy trading, as well as anti-money laundering and regulatory activities. Jesús has over twelve years of experience in risk management, internal audit, and fraud investigations, where he has specialized in the application of data science and machine learning methods to optimize risk control activities and examinations.

14:00 – 15:00

Stress testing credit risk portfolios
10:15 am US / 3:15 pm UK

15:15 - 16:15

  • General principles of forecasting models
  • Why undertake stress testing
  • Credit risk losses in a recession
  • Model design
  • Regulatory expectations
  • Use in income stress testing
Raffaella Calabrese

Senior Lecturer (Associate Professor) in Data Science

Business School University of Edinburgh

Raffaella is Associate Professor in Data Science at the University of Edinburgh Business School. She is a member of the Credit Research Centre and part of the Fintech team at the Edinburgh Future Institute. 

Her research and collaborations with industry (such as Moody's Analytics, Nationwide, RBS, Barclays, SAS and Bank of England) are focused on developing new models for analysing credit risk and Open Banking data. The former includes scoring models, modelling of loss given default, stress testing, interpretability and use of alternative data (e.g. social media, email or mobile data) in the credit risk framework. For Open Banking, she has proposed novel solutions for affordability test and credit scoring. 

Raffaella holds a BSc in Economics from Bocconi University and a PhD in Statistics. She has conducted research at the Wharton School of the University of Pennsylvania, Louisiana State University, Luigi Bocconi of Milan, University College Dublin and ETH Zurich

15:00 – 15:15

Break
10:00 am US / 3:00 pm UK

15:00 - 15:15

15:15 – 16:15

Model validation and assurance
10:15 am US / 3:15 pm UK

15:15 - 16:15

  • Model validation framework
  • Building and maintaining a framework to validate credit risk portfolio models
  • Macro-economic variable choices
  • Vendor models
  • Future considerations
Raffaella Calabrese

Senior Lecturer (Associate Professor) in Data Science

Business School University of Edinburgh

Raffaella is Associate Professor in Data Science at the University of Edinburgh Business School. She is a member of the Credit Research Centre and part of the Fintech team at the Edinburgh Future Institute. 

Her research and collaborations with industry (such as Moody's Analytics, Nationwide, RBS, Barclays, SAS and Bank of England) are focused on developing new models for analysing credit risk and Open Banking data. The former includes scoring models, modelling of loss given default, stress testing, interpretability and use of alternative data (e.g. social media, email or mobile data) in the credit risk framework. For Open Banking, she has proposed novel solutions for affordability test and credit scoring. 

Raffaella holds a BSc in Economics from Bocconi University and a PhD in Statistics. She has conducted research at the Wharton School of the University of Pennsylvania, Louisiana State University, Luigi Bocconi of Milan, University College Dublin and ETH Zurich

14:00 – 15:00

Model risk management
9.00 am US / 2:00 pm UK

14:00 - 15:00

  • Regulatory compliance
  • Corporate
  • Retail
  • TRIM guidelines
  • IFRS 9
  • Long exposures

15:00 – 15:15

Break
10:00 am US / 3:00 pm UK

15:00 - 15:15

15:15 – 16:15

Effects of COVID-19 on models
10:15 am US / 3:15 pm UK

15:15 - 16:15

  • Possession proceedings
  • Technical default
  • How stressed will the models get?
  • Design limits based on historical data
  • Managing model performance

14:00 – 15:00

Climate risk
9.00 am US / 2:00 pm UK

14:00 - 15:00

  • Climate risk in relation to credit risk modelling
  • Credit risk implications
  • Corporate sustainability

15:00 – 15:15

Break
10:00 am US / 3:00 pm UK

15:00 - 15:15

15:15 – 16:15

Forward looking approach to credit risk modelling
10:15 am US / 3:15 pm UK

15:15 - 16:15

  • Realistic and worst case scenarios
  • New modelling approaches
  • How to interpret changes to frameworks
  • Government policy in credit risk models and capital release