Presenting Deep Analytics at Oxford University

Thrilled and honored to introduce Deep Analytics for Oxford University in Central London, Thursday 12th December 2019.

RiskMinds 2019

Flying Monday to Amsterdam with my colleagues from Superfly Analytics of Danske Bank, including Brian Huge and Ove Scavenius, to attend the RiskMinds 2019 risk management conference and the award ceremony, where our group is nominated for ‘Excellence in risk management and modeling’.

EDIT: Superfly Analytics now won the award:

Excellence in Risk Management and Modelling, winner: Superfly Analytics at Danske Bank

We will be presenting our vision of modern risk management systems and the ‘One Analytics’ platform: full front to back consistency with scripting of cash-flows , model hierarchies and AAD. Further, we will present ‘Deep Analytics’: leveraging risk management systems with AI to learn revaluation and risk analytics on the fly. For those unable to attend, we posted our slides online here:

www.deep-analytics.org

click on the picture to see the presentation

Article with code, Wilmott Nov19

My paper “Computation graphs for AAD and Machine Learning part I: Introduction to computation graphs and automatic differentiation” was just published in Wilmott, the first in a series of articles with code dedicated to Computational Finance. It covers computation graphs, backpropagation, AAD and implementation in finance, taking inspiration in the recent achievements of the Superfly Analytics group of Danske Bank.

A. Savine, “Computation graphs for aad and machine learning part i: introduction to computation graphs and automatic differentiation,” Wilmott, vol. 2019, iss. 104, p. 36–61, 2019.

Deep Analytics

By Brian Huge and Antoine Savine

Deep learning derivatives pricing, and risk, with differential regularisation

See our slides here:

http://www.deep-analytics.org

Recorded workshop from Kings College London: AAD, Backpropagation and Machine Learning in Finance

Back in March, I gave a series of lectures at Kings College London on automatic adjoint differentiation, backpropagation and machine learning, and how it all connects and applies to risk management of financial derivatives.

The lectures were recorded and made freely available online, either from Kings own page:

https://nms.kcl.ac.uk/probability/workshopPages.php?id=8

or, on YouTube:

It is my understanding that the audience at Kings found the talks useful, and I hope that they may be beneficial to a wider audience on the internet.