The Problem
Financial institutions face significant computational challenges in managing counterparty credit risk. Calculations of XVA, IMM capital, PFE, and CVA capital require revaluing vast portfolios of OTC derivatives across thousands of Monte Carlo scenarios and time steps—resulting in millions, or even billions, of portfolio revaluations. This makes CCR pricing and risk evaluation highly time-consuming and computationally expensive, often leading to low quality CCR numbers, delays in decision-making and substantial infrastructure costs.
The Solution
MoCaX Intelligence transforms counterparty credit risk calculations with advanced AI, Machine Learning and algorithmic acceleration.
This technology enables real-time, highly efficient calibration of an intelligent replica of the pricing model for each trade within a portfolio—integrated seamlessly into every CCR Monte Carlo run. This enhancement works within existing infrastructure, significantly improving the performance of current CCR engines without requiring costly system overhauls.
Smart Sampling Machine Learning
During each CCR computation, the Monte Carlo simulation space (composed by, say, 10,000 scenarios) is strategically explored “on-the-fly”, generating a select set of “Smart” scenarios; typically, around 50 Smart scenarios. From these, a high-precision Machine Learning function is constructed that delivers high-precision and is ultra-fast speeds. This is done by leveraging the exponential convergence of Chebyshev Tensors.
This Machine Learning pricing function is then used to evaluate the entire portfolio across millions or billions of scenarios with unparalleled accuracy, achieving up to 100x acceleration while maintaining precision.
This not only speeds up risk assessments but also drastically reduces cloud computing costs.
Examples
IR Swaps
The graphs below present EPE and PFE profiles for 1, 5, 10, 15, and 20-year IR swaps. We compare results from a full production-standard IR swap pricing model versus MoCaX’s AI-powered pricing. Note that each apparent line in the graphs is, indeed, two lines: the MoCaX solution is so precise that its outputs are visually indistinguishable from the traditional pricing model.
In this realistic simulation, MoCaX’s ML functions was calibrated from only six “Smart” scenarios—instead of brute-force calculations across all 10,000 Monte Carlo scenarios. As a result, MoCaX achieved an astounding 1,666x acceleration, reducing cloud computing costs proportionally.
Bermudan Swaptions
Bermudan Swaptions are typically valued using Monte Carlo methods, meaning a full CCR computation require Monte Carlo within Monte Carlo techniques, or alternative methods with limited accuracy.
The graph below illustrates EPE and PFE profiles for a 5-year Bermudan Swaption, comparing traditional full revaluation with MoCaX’s optimized approach. Once again, the results are visually indistinguishable, underscoring the accuracy of MoCaX’s Smart Sampling Machine Learning model.
For this simulation, MoCaX calibrated its model at each Monte Carlo time step using just 50 sampling points, achieving a 200x acceleration while maintaining high full-revaluation accuracy and reducing cloud compute demand most substantially.
Exotic Equity Barrier
For the final case, we examine an Exotic Equity Barrier, which requires Monte Carlo-based pricing and, consequently, Monte Carlo within Monte Carlo computations for CCR calculations.
As with the previous examples, MoCaX’s results are indistinguishable from the brute-force Monte Carlo approach. MoCaX’s Smart Sampling Machine Learning models were dynamically calibrated using 96 sampling points, delivering a 105x computational speedup while preserving precision.
Conclusion
MoCaX Intelligence transforms counterparty credit risk assessment by integrating cutting-edge AI and Smart Sampling Machine Learning. Our solutions deliver unprecedented speed, accuracy, and cost efficiency, allowing financial institutions to make faster, data-driven risk management decisions while optimizing infrastructure costs.