BMO is using Riskfuel AI to accelerate Structured Note Pricing and Scenario Analysis
Bank of Montreal, Sept 2021
It’s official! BMO is a Riskfuel customer. After a successful pilot tackling one of their most complicated valuation models, we are joining forces to accelerate a whole slate of models used by the structured products desk.
Eureka! The XVA Compute Challenge Solved
Wilmott Magazine, Sept 2021
XVA sees your trading book valuation problem and raises it by whatever magnitude induces hyperventilation. Ryan Ferguson discusses how a moment of inspiration led to total domination of the XVA compute problem
BMO Financial Group Taps Riskfuel Analytics
Wilmott Magazine, Sept 2021
BMO Financial Group, the 8th largest bank, by assets, in North America, and Riskfuel Analytics a Toronto-based start-up, have announced a partnership to develop models for pricing and scenario analysis of structured derivatives transactions.
How XVA quants learned to stop worrying and trust the machine
Risk.net Magazine, July 2021
Initial scepticism about using neural networks for derivatives pricing is giving way to enthusiasm. Last year, Scotiabank began using a deep neural network developed by Riskfuel, a fintech start-up, to approximate the outputs of the Monte Carlo models it uses for derivatives pricing.
How Riskfuel is using Inlets to build machine learning models at scale
In this blog, we’ll show how Riskfuel is using Inlets to securely oversee fully remote and hybrid cloud deployments.
Arbitrage-Free Implied Vol Surface Generation with Variational Autoencoders
We propose a hybrid method for generating arbitrage-free implied volatility surfaces consistent with historical data by combining model-free Variational Autoencoders with continuous time stochastic differential equation driven models.
Riskfuel Joins OnRamp Insurance Accelerator
Riskfuel has been selected to take part in gener8tor’s OnRamp Insurance Accelerator in partnership with Allianz Life and Securian Financial. Riskfuel is one of five startups chosen from over 500 applicants to participate in gener8tor’s three-month, concierge accelerator program.
Completing Partial Implied Vol Surfaces with Variational Autoencoders
In this talk, we will explain how variational autoencoders can remove human bias from this procedure and let the data speak for itself through unsupervised learning.
Riskfuel Wins WatersTechnology's Best Sell-Side Newcomer Award
Riskfuel has been awarded the Best Sell-Side Newcomer award by leading industry journal WatersTechnology. Judged by a panel of industry experts from across the market, the highly valued Sell-Side Technology Awards acknowledge excellence in trading technology. Riskfuel stands in the winners’ circle with other winners including Bloomberg and Numerix.
Deeply Learning Derivatives: from Hilbert to Riskfuel
In this talk, we will explain how graphical solvers of Hilbert’s day fit into the modern deep learning framework and ultimately allow us to build networks that replicate the solutions operator of stochastic differential equations governing the valuation of high dimensional contingent claims.
Hands-Off Approach to Completing Implied Volatility Surfaces
A type of deep neural network called the variational autoencoder is used to automatically complete partial volatility surfaces.
Variational Autoencoders: A Hands-Off Approach to Volatility
Variational autoencoders can be used to construct a complete volatility surface when only a small number of points are available without making assumptions about the process driving the underlying asset or the shape of the surface.
Riskfuel Wins FIA Innovator of the Year Award
Riskfuel was one of nine startups selected to participate in the annual Innovators Pavilion at the FIA Expo. The startups were evaluated on the degree of innovation, the relevance to derivatives markets, and the potential impact on the industry and judges chose Riskfuel for “its innovative solution to the challenges of derivatives valuation”.
Riskfuel Customer Wins Top Risk.net Award
Scotiabank wins Risk.net’s Technology Innovation of the Year. In partnership with tech vendor Riskfuel, the bank has trained deep neural networks (DNNs) as a valuation methodology within the framework. Put into practice, the platform’s web interface allows the bank’s front office to run XVA analysis with real-time data on either existing active trades or ‘what-if’ trades with potential clients.
Ultra-fast and Accurate Derivatives Pricing with Deep Learning
Technical details to accompany the article written by Ian Finder of Microsoft.
Deep Learning to Jump
We describe a Jump Unit that can be used to fit a step function with a simple neural network. Our motivation comes from quantitative finance problems where discontinuities often appear
Exploring Riskfuel's Bermudan Swaption Pricing Demo
This paper describes the Bermudan Swaption and its valuation models, and discusses a small case study to illustrate the accuracy of the Riskfuel model and compare its run-time performance again the target Quantlib model.
Microsoft: Riskfuel Technology is 20,000,000x faster
Ian Finder, Microsoft, April 2020
Results of Microsoft collaboration with Riskfuel to measure performance of Riskfuel-accelerated production models.
Deeply Learning Derivatives: The Paper that Started It All
This paper uses deep learning to value derivatives. The approach is broadly applicable, and we use a call option on a basket of stocks as an example. We show that the deep learning model is accurate and very fast, capable of producing valuations a million times faster than traditional models.
1,000,000x faster models: how it works
Deep neural networks can be trained to learn a functional approximation of derivatives valuation models that use mathematic simulations. Riskfuel AI-based technology cuts the computation costs to virtually zero allowing for on-demand recalculation of portfolio values and a complete up-to-the-second view on risk