IFRS 9 for Insurers: Implementing a Robust, Efficient and Transparent Methodology
Date:
October 17, 2019
Duration:
30 minutes
Location:
On-Demand
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During this 30 minute webinar, gain a practical demonstration to produce the new expected credit loss (ECL) calculations as required by International Financial Reporting Standard 9 (IFRS 9), to avoid the black box effect.
Some key themes we will discuss:
How you can combine internal information with our data and analytics to meet IFRS 9 requirements
Why the use of sophisticated loss given default methodologies is absolutely crucial for efficient allocation of reserves/provisions.
How you can best utilise scarce resources in order to generate accurate IFRS 9 expected credit losses
Why a robust and transparent methodology is key to successful audits post-implementation.
Tauqir AslamDirector, Risk Services, S&P Global Market Intelligence
Tauqir is a director in the Risk Services team, based in London. He focuses on assisting credit-sensitive institutions (including banks, insurers, governments and funds), operating globally, in improving their internal rating systems for all low default asset classes, including corporates, financial institutions, specialised lending and public finance. He specialises in delivering regulatory, pricing and financial reporting (IFRS 9) solutions. Full Bio
Tauqir is a director in the Risk Services team, based in London. He focuses on assisting credit-sensitive institutions (including banks, insurers, governments and funds), operating globally, in improving their internal rating systems for all low default asset classes, including corporates, financial institutions, specialised lending and public finance. He specialises in delivering regulatory, pricing and financial reporting (IFRS 9) solutions.
Tauqir is also in charge of managing the Risk Services’ loss given default (LGD), exposure at default (EAD) and validation related solutions, and is a regular speaker at Risk Services LGD thought leadership events. He has 15 years’ experience in managing and delivering complex consultative assignments related to all aspects of credit risk.
Tauqir holds a degree in Mathematics and Management from Kings College London. Minimize
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