Posts

Article with code, Wilmott Jan20

The second part of my paper  “Computation graphs for AAD and Machine Learning part II: Adjoint differentiation and AAD” was just published in Wilmott, the second in a series of three articles with code dedicated to AAD and Computational Finance in general. It covers computation graphs, backpropagation, AAD and implementation in finance, taking inspiration in the recent achievements of the Superfly Analytics group of Danske Bank.

The first part was published in Wilmott November 2019 issue.

A. Savine, “Computation graphs for aad and machine learning part ii: adjoint differentiation and AAD” Wilmott, vol. 2020, iss. 105, p. 32-45, 2020.

Excellence in Risk Management and Modelling award | RiskMinds 2019 | Winner: Superfly Analytics at Danske Bank

From Risk Minds web page: “This award is for the team who has tirelessly designed, developed and implemented new models and risk management techniques that have helped their financial institution manage risk more accurately and/or helped their institution comply with regulatory requirements.”

RiskMinds is “the world’s largest risk management event. 700+ CROs and experts from banks, buy-side, regulators, academia and beyond cover every hot topic in risk.”

Superfly Analytics presented its One Analytic Engine and Deep Analytics: Risk Management with AI. See all the slides here.

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.