Jan Jelovsek
ING
Head of integrated risk
ML-based Regulatory Models
- Current market practices.
- Challenges and benefits of the use of ML-based IRB models.
- Possible use cases for ML in IRB Models incl. optimization of linear models with ML methods, development of challenger models, and fully fledged operational ML models.
- Position of the regulators regarding the use of ML for IRB models incl. implications of the EU AI Act.
Jurij Fedinec
Bawag
Head of Credit Risk Portfolio Quantification
Estimation of Credit Conversion Factor (CCF) for Off-balance Sheet Exposures
- How is CCF determined?
- Regulatory requirements vs. Business needs.
- Modelling of CCF under A-IRB approach.
Nal Mantravadi
Revoult
Head of Credit Risk
Credit Risk Scoring for Fintechs : An Australian context
- Credit risk landscape in Australia.
- Fintech specific challenges.
- Current solutions and outlook for the future.
Fiorella Salvucci
Intesa Sanpaolo
Executive Director - Head of Credit Risk Management Department
How compliance Will Be Impacted by the Use of AI in Making Credit Risk Decisions Both Online and Offline?
- AI algorithms must be transparent and explainable to meet regulatory standards.
- Compliance requires strict adherence to data privacy regulations.
- Close monitoring by regulatory bodies necessitates compliance with guidelines.
- Clear explanations for AI-driven decisions enhance compliance.
Alexandar petrov
Nordea
Head of Group Credit Risk Corporate and IRB models unit (Leader of Leaders role, 25+ FTE)
Implementation of Margin of Conservatism (MOC) framework to address regulatory requirements of EBA/GL/2017/161 for IRB credit risk models
- Implementation of MoC framework to address regulatory requirements of EBA/GL/2017/161 for IRB credit risk models will be discussed with practical examples.
- MoC concept according to EBA GL: Overview
- MoC A MoC B MoC C
- MoC special case: Ranking deficiencies
- Qualitative MoC
- MoC Aggregation
- Cyclicality/Rating philosophy as a source of MoC/add-on
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