Credit Risk Management and Modelling, Frankfurt
Understand how to manage credit risk within your organisation; with a focus on modelling methods, stress testing and machine learning.
This course is designed for anyone with credit risk responsibilities within financial services. Key sessions will cover the impacts of Basel IV and TRIM, approaches to credit risk modelling, stress testing credit risk portfolios and applications of AI and machine learning.
Best practice approaches to managing credit risk post IFRS 9
How Basel IV, IRB and TRIM interact and can be managed together
A new or improved understanding of credit risk modelling
Methods for assessing non-modelled credit risks
How to manage stress testing your credit risk portfolios
Applications for machine learning and AI
Credit risk management post IFRS 9
Basel IV, IRB and TRIM
Structural credit risk models
Best practice credit risk modelling
Non-modelled credit risks
Model validation and assurance
Stress testing credit risk portfolios
Machine learning and AI in credit risk modelling
Senior Manager | Financial Services Risk Consulting
Dr. Zhilin Yao
Head of Risk Controlling
Agricultural Bank of China, Frankfurt
Dr. Zhilin Yao is Head of Risk Controlling at Agricultural Bank of China Frankfurt since 2013, who has over 10 years of risk experiences on different organization levels. Following his Doctor’s degree in quantitative skills and financial economics, he worked in Credit Risk Methodology/Risk Steering team at Dresdner Bank/Commerzbank (2008 -2011). He continued his career in ICAAP and Group Strategic and Capital Planning at Deutsche Bank (2011 -2013). Next to his risk job he gathers knowledge about legal as a candidate in LL.M. Finance.
Institute of Theoretical Physics at WWU Münster
2001 – today: Privatdozent, Institute of Theoretical Physics at WWU Münster
2018 – today: DZ HYP (part time job in credit risk after retirement, since Sept. 2018)
Stefano Bonini PhD, CStat, PStat®
Executive, finance and risk
Stefano is a Management Consulting Director of Accenture Finance & Risk and since 2011 has led the development of Accenture Credit Risk Modeling & Validation and Model Risk offering in Italy. He built strong relationships with key clients as Banks and Rating Agencies, being in charge of numerous Risk Modeling, Validation & Model Risk projects mainly within regulatory framework of Basel II/III/IV, IFRS9, and TRIM. He also acted as advisor risk culture formation for board members of Banks under SSM. He also lead the Italian Risk Management Association Study Group on Machine Learning.
Stefano is adjunct professor of Banking and Risk Management at top tier Italian Universities like University of Bologna & MIP Business School, holds a PhD in Banking & Finance and is currently the only person with triple Certified Chartered Statistician: from USA, UK & Italian Statistical Association.
He is regular speaker to industry events & international conferences on Risk Management, is author of publications on books and international journals (Journal of Credit risk, European Journal of Finance) and collaborated with CONSOB within the "Financial Education Month”.
Expert in ML and development of cloud microservices services
Dominik Zabel studied International Economics as well as Finance with main focus on quantitative methods before joining Deloitte in 2018.
His field of work at Deloitte is related to credit risk modeling, in particular, the development and practical implementation of advanced credit risk models using standard and machine learning approaches.
On his previous projects, he implemented different machine learning algorithms for bond risk and provided the modeling services as micro services in a cloud based infrastructure using Docker container.
Manager credit risk & lead of ML lab
Marcel Jäger has more than seven years of experience regarding PD, LGD, IFRS 9 and machine learning modeling and implementation. He is the leading expert at Deloitte for the application of machine learning in the credit risk environment and leads a lab which builds machine learning prototypes for financial institutions. Since august 2017, Marcel is external lecturer for the modeling of quantitative credit risk at bFi Vienna
In his last project, he was the head of data science of a fintech and responsible to hire a team, build a cloud based infrastructure and implement credit risk models for bond risk using machine learning models and >3 terabyte of data.
Principal supervisor, supervisory policy division
European Central Bank
Luca Ciavoliello joined the Supervisory Policy Division of the European Central Bank on 1 December 2018. He is an accounting expert, in charge of leading the ECB horizontal supervisory activities on IFRS 9 for the largest banks of the euro area.
Mr Ciavoliello started his career at Deloitte in 2006 in the Financial Sector Industry, where he took part in audits and financial due diligences on banks and financial institutions. He joined Banca d’Italia in 2012 as policy expert in the Regulation and Macroprudential analysis Directorate; he represented Banca d’Italia in various technical working groups on accounting at the European Banking Authority and in the Basel Committe.
Luca holds master degrees in Banking and Finance and in Economics and Business Administration, and he is author of publications on NPLs and complex financial instruments.
Kaan H. Aksel
Director, regulatory management - quant team
Key expert in the PwC global network in developing and introducing rating models and analytical framework for assessing credit risk in accordance with Basel III and Basel IV for more than 20 years. Kaan is one of the leading PwC's European experts in the area of Risk Modelling & RWA optimization, being engaged in several international PwC’s projects related to risk management and measurement at various financial institutions. The Quant Team with more than eighteen expert from PwC Frankfurt, he is leading, specialized in the Regulatory questions of model development in the context of the new requirements imposed by the EBA and ECB.
Kaan has vast experience in delivering projects related to implementing Basel requirements, such as:
CPD / CPE Accreditation
This course is CPD (Continued Professional Development) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event.
This course is CPE (Continuing Professional Education) accredited and will allow you to earn up to 12 credits. One credit is awarded for every hour of learning at the event in accordance with the standards of the National Registry of CPE Sponsors.
Themes include; data, capital, AI, climate risk and revers stress testing.
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