Machine Learning for Risk Calculations

We love the challenge of seemingly impossible calculations
Chebyshev Tensors


  • Acceleration of Counterparty Credit Risk simulations – XVAs, PFE, IMM capital
  • Acceleration of ES calculation in  IMA FRTB
  • Reduction of computational cost in Risk Calculations
  • Simulation of sensitivities inside a Monte Carlo engine
  • Dynamic Initial Margin (DIM) simulation
  • Portfolio optimisation algorithms
  • Balance Sheet optimisation
  • Pricing function cloning from pricing libraries to separated risk engines


The solutions we have designed are grounded on a number of Machine Learing techniques, Deep Neural Nets and Chebyshev Tensors


There are a number of resources that we share so you can run your own research and investigate what you need

MoCaX Library

Our library is available for free download. Build Chebyshev Tensors with it

Who we are

We are a bunch of geeks that feel passionate about computational challenges

Contact us

We love hearing from other researche, share ideas, answer questions

In this book, I. Ruiz and M. Zeron share the line of research they have taken for several years on the topic of optimising the computation of risk calculations.


Out in Q3 2021