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【IAA】ASTIN Masterclass Webinar - Model Risk Management – The Quest for a Unifying Approach by Andrew Smith
The webinar on June 26th with Andrew Smith is free!
日期: session 1： 6/26(三) 4pm – 5.15pm (Taipei Time)
session 2： 6/27(四) 0am – 01.15am (Taipei Time)
方式: 視訊會議 Zoom
In this webinar, Andrew will introduce his Masterclass on Model Risk Management.
He will preview the first episode in this 8-part series and then delve further into the main take-aways covered throughout the Masterclass, such as: underwriting model risk; reserving model risk; investment model risk; and, model risk management processes.
We will close with a Q&A session for the audience.
If you are working in Risk Management, you should attend this webinar!
Model Risk Management – The Quest for a Unifying Approach by Andrew Smith
Modern financial businesses rely on thousands of models to support decision-making from pricing and reserving through risk and capital to management bonuses and shareholder decisions.
These models sometimes fail. Forecasts prove to be inaccurate, or decisions supported by models may turn out to be unwise. What can we do about this? We cannot eliminate the possibility that the future turns out differently to a model prediction. However, we can ensure that assurance we give on models is both truthful and statistically meaningful. We can reverse stress-test models by feeding them awkward simulated data until they break down.
We can choose between harsh validation tests that reveal model weaknesses, or we can apply powerless validation methods where a green light is a foregone conclusion. We can foster a culture where people who become aware of model shortcomings are heard rather than silenced.
This ASTIN Masterclass uses a series of examples to highlight quantitative approaches to model risk management, using examples related to underwriting risk, stochastic reserving and the modelling of asset price changes.
Andrew offers tips for actuaries pressured into expressing undeserved confidence in risky models, together with tips better to support decision making in the context of uncertainty.