9.00 am - 11.00 am - GMT
(10.00 am - 12.00 pm CEST)
Deep learning techniques represent a certain part of wider machine learning methods and have become increasingly popular for a variety of real-life applications solving complex high-dimensional problems.
So far, typical applications for deep learning architectures such as deep neural networks and recurrent neural networks include speech and pattern recognition, language processing, audio recognition or machine translation. In all these applications, deep learning techniques were able to yield break-through results due to their highly flexible and innovative architectures and their approach of training models towards a set of given data.
Newly emerging generative techniques such as generative adversarial networks or variational autoencoders which had originally been developed for image generation purposes allow for powerful applications in the field of risk modelling and model validation.
The registration fee for the webinar is € 100.00 plus 19 % VAT only.
To make a registration, please click here.
The webinar will introduce generative deep learning techniques in general and then present several use cases applying them to typical tasks from risk modelling and model validation.
We will demonstrate where the sweet spot of these techniques is and how these techniques can be used to validate or replace currently used methods and models or applied in combination with established statistical methods.
The webinar will contain detailed case studies for demonstrating the capabilities of generative deep learning methods, such as applications to copulas, multivariate risk factor distributions, risk factor dimension reduction and detection of data errors.
- - Introduction to deep learning and neural networks
- - Detailed presentation of newly emerging generative modelling techniques:
- - Generative adversarial networks
- - Variational autoencoders
- - Applications of generative modelling techniques to various risk modelling and model validation topics
- - Case Study 1: Deep copula – applications of generative models to multivariate risk factor models
- - Case study 2: Using autoencoder for dimension reduction and data error detection
Dr Mario Hoerig, Partner, Oliver Wyman Actuarial
Mario Hoerig is a Partner with Oliver Wyman, co-leading the actuarial services offering in the German speaking markets. Mario focuses on quantitative modelling under Solvency II (economic scenario generators for risk-neutral and real-world purposes, ALM studies, risk factor modelling for Solvency II, risk aggregation, economic capital and capital management) and advises some of the largest insurance companies in Europe on these topics.
Dr Daniel Hohmann, Senior Manager, Oliver Wyman Actuarial
Daniel Hohmann is a Senior Manager with Oliver Wyman. He has a strong quantitative background and has been advising his clients on a variety of market risk and economic capital topics such as proxy modelling, risk-neutral and real-world scenario generation and time series analysis for market data.