Ensuring speedy accuracy in financial risk assessment

How to bring your financial risk assessment to the next level? Quside’s new RPU can help you gain rapid and accurate results in your financial simulations by accelerating your stochastic workloads in creating increased scenarios and outcome accuracy. Uncover the benefits of Quside’s RPU today and witness the next tier of speed and computational efficiency of your simulated risk scenarios.


Banks and financial institutions are a vital part of today’s economy. In a sense, they act as the circulatory system of the productive economy, transferring capital to where it is most needed and thereby enhancing society’s economic growth and welfare.

This highly relevant role requires the values of effectiveness and efficiency to drive the activities of banking and financial institutions. Therefore, for any institution, the continuous improvement of operational processes is a daily challenge.

In addition to this continuous improvement, everyone in the sector is currently exposed to a whole series of challenges, many of which require using new technologies to overcome.

Compliance with new regulations is a clear example of such challenges, as a central issue for all financial institutions since the financial meltdown of 2008, which forced an increase and reinforcement of existing legal constraints for financial institutions. Regulations such as Basel III or the Dodd-Frank Act have substantially increased costs for financial institutions. The more accurate risk estimation required by these regulations demands a more significant computational burden in the form of a much larger number of simulations linked to increasingly complex market models.

Likewise, the entry on the scene of many new competitors, such as Fintech companies, together with an exclusive customised offer of financial products, requires enormous responsiveness and adaptability from financial institutions to respond efficiently and in a personalised way to customer demands. Enabling these offerings also requires a tremendous computational capacity.

As the above use cases indicate, innovation becomes the main driver of such improvements to maintain the effectiveness, and efficiency of financial transactions and thus maintain sustained growth of the sector. 

Financial institutions can now leverage better performing randomness acceleration solutions for a broad range of risk calculations such as Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR). 

Quside’s physical or cloud device, Randomness Processing Unit (RPU One), aims to greatly accelerate simulated scenarios in Value-at-Risk assessments. An improved methodology that offers a more accurate risk estimation from an increased number of scenarios at a fraction of the time. 

This white paper explains the importance of  simulation and optimization workloads that rely on stochastic processes, including the Monte Carlo method. As they require an ever-increasing source of high-quality, high-speed random numbers, it’s crucial that they get the speed and quality  they need to successfully compute outcomes for financial institutions, highlighting its strategic advantages over existing solutions. It concludes with presenting Quside’s physical and cloud-based randomness processing device, The RPU One, as the superior choice in simulating  accelerated scenarios for the benefit of optimisingValue at risk (VaR) & conditional Value at Risk (CVaR) using stochastic computational techniques.

Understanding Randomness: Why is it significant

Randomness is an intrinsic facet of the universe, encompassing unpredictability and chance. It underlies the natural world, shaping everything from quantum particle behaviour to the outcome of a coin toss. While inherently elusive, randomness is fundamental to diverse fields like statistics and probability, cryptography, and quantum computing. When embracing randomness, we can navigate uncertainties, adapt to unexpected outcomes, and even find creative inspiration in the unpredictable. 

Using randomness to model complex and unpredictable scenarios

Risk/value scenario simulations using randomness hold immense potential for the field of finance. The more accurate and fast the randomness is, the more powerful the modelling of the complex financial systems, feeding into managing risk and optimising investment strategies.

In finance, uncertainty and risk management are paramount. Randomness, a key element of Monte Carlo simulations, plays a pivotal role in assessing and quantifying these risks. In other words, stochastic processes model the randomness and unpredictability of financial markets, which is essential for developing profitable strategies. 

Value at risk in measuring and controlling risk exposure

Value-at-Risk (VaR) is a widely used risk metric in finance that quantifies the potential loss in value of an investment portfolio over a specified period for a given confidence interval. Risk managers use VaR to measure and control the level of risk exposure, applying VaR calculations to specific positions or whole portfolios or even using them to measure firm-wide risk exposure.

Essentially, VaR offers an estimate of the worst-case scenario for losses, given a specific level of probability which is fueled by the stochastic nature of the process. For instance, it would be good to know if a portfolio has a one-day VaR of $1 million at a 99% confidence level. This means that there is a 1% chance that the portfolio will decline in value by more than $1 million over a single day. VaR, thus, provides a quantifiable decision-making tool to assess potential losses, making it easier for financial professionals to communicate and understand risk levels.

The three types of VaR Modelling

There are many types of models employed in the financial industry, and institutions place a premium on reducing model risk based on randomness. This refers to the potential for adverse consequences from decisions based on incorrect or misused risk model results and reports.

Here are some of the most commonly used VaR models:

Monte Carlo Simulation

The Monte Carlo simulation uses computational models to simulate projected returns over hundreds or thousands of possible iterations, where each iteration uses a different set of random evolutions. A quantifiable VaR is then derived from the distribution of all the trajectories calculated. The Monte Carlo approach is flexible and can be used in complex portfolios with multiple types of instruments. It doesn’t rely on historical returns or strictly parametric assumptions, making it adaptable to various scenarios.

Parametric Value at Risk

Parametric VaR assumes that returns are normally distributed or follow another specific distribution. This approach uses the mean and standard deviation of returns and, based on the chosen distribution, calculates VaR. It’s computationally less demanding but contains a drawback that it might not always capture «tail risks,» or so-called Black Swan events accurately.

Historical Value at Risk

Historical VaR directly uses historical price data to estimate potential future losses. For instance, when calculating a one-day VaR using a year’s data, risk managers can observe how the portfolio would have performed on each day given the previous day’s position. From there, they can compute the loss distribution from these data points.

Irrespective of the Value at Risk model, the aim is the same: reducing risk characteristics in investment decisions further connecting with regulatory demands.

The Role of randomness acceleration in adhering to Basel IV Standards

Pivoting to Randomness Acceleration is not only a performance consideration but also a potential regulatory one. Basel IV is a set of international banking reforms whose aim is to improve the quality of banking supervision worldwide. Measuring and managing risk is an important part of these reforms, which seek to maintain financial stability and prevent future financial crises. Financial institutions must gear up their operations before these rules come into force in 2025.

Financial institutions are required to adopt Basel IV standards imminently before they are expected to become standard practice by 2025. Randomness Acceleration is not directly stipulated in Basel IV standards. However, Basel IV requires the use of more strict risk metrics than Basel III, which are also more computationally intensive, such as the stressed Expected Shortfall simulations. These metrics are more sensitive as well to the accuracy in estimating rare events, so having a high-performance, high-quality randomness source for the simulation of these models can be critical, especially for those simulations for which sampling becomes the bottleneck.

Regulatory bodies can scrutinise VaR models and their underlying assumptions. Thus, using Randomness Acceleration as a basis for simulated scenarios can increase confidence in the robustness of these models, both from an internal risk perspective and a regulatory standpoint.

The limitations of VaR Simulations

The main problem encountered in simulations for value-at-risk, conditional value-at-risk, or any other risk metric, is the complexity of simulating the returns of the various products that make up the investment portfolio. In many of these cases, the investment portfolio must be simulated not through more common procedures, such as historical simulation or the use of simple models, but rather by requiring a complex simulation with high computational requirements, like Monte Carlo simulation.

These simulations demand the use of models that can adequately reproduce the behaviour of the various market variables to be simulated within these models, approximating the behaviour of financial assets through the simulation of a stochastic process. This process allows for the simulation of the evolution of financial asset prices over time.

The primary computational challenge in Monte Carlo simulations lies in the need to generate random numbers that conform to a specific distribution. This operation may turn  computationally expensive, and in certain situations, the generation of random numbers can account for more than half of the total time required to simulate a financial product. As financial markets become more complex and the volume, velocity, and variety of data grows, VaR simulations need to be more defined.

In light of these inefficiencies, The Randomness Processing Unit offers a superior tool for financial simulations, given their ability to generate truly random numbers at speed, potentially addressing some of the aforementioned challenges.

Quantifying the performance of Quside RPU One: speed and accurate

The Quside RPU One, available both as physical processor and cloud version, is a hardware accelerator developed for the most demanding randomised workloads. The RPU offloads the most demanding randomness-related computational tasks, from generation to distribution processing, to deliver increased efficiency in the overall simulation time. By offloading in a dedicated hardware, there are two advantages. 

First, the randomness processing unit is accelerated by leveraging hardware means. Second, the computing device consumes less resources and can process more valuable information in a fraction of the time. This acceleration vastly improves VaR and CVaR applications.

Industry: Financial services
Use case: Risk assessment & Monte Carlo simulations

Low randomness generation speed
Error-prone estimations for VaR and ES

Up to 10x speed-up and 20x energy savings
More scenarios –> better accuracy
Faster convergence to pricing

Benefits and use cases:

  • Asset valuation and pricing
  • Monte Carlo acceleration
  • Portfolio Optimisation
  • Dynamic pricing
  • FRTB – Basel III compliance
  • Fraud detection
  • High Frequency Trading

In addition to the acceleration capabilities, the RPU One can also ensure entropy quality by using an embedded quantum random number generation chip and a dedicated Metrology IP Core. A tool that enables continual quality assurance of randomness to produce the most accurate risk assessments in cases like Value at Risk and Expected Shortfall.

Quside RPU One integrates a 400Mbps quantum entropy source that continuously fast reseeds the accelerator to provide 10 Gbps randomness distributions whether normal, uniform, or logarithm, with other distributions added as per customers’ feedback. For even higher speeds and available soon, the second generation of RPU One incorporates a 1Gbps quantum entropy capable of continuously fast re-seeds to provide 20 Gb/s  with the same distribution algorithms.

Thus, Quside’s Randomness Processing Unit achieves 10x speeds from the first generation and 20x acceleration from the second generation compared to traditional PRNG methods. This speed ensures that simulations and optimisations have a continuous supply of truly random numbers without lag or delay as a dedicated device is allocated to this crucial randomness generation.

By adding several additional algorithms and developments on top of our entropy sources, our randomness acceleration platform allows our devices to be used as hardware accelerators for running the most demanding stochastic workloads. These include all the generation and post-processing of the most used distributions in all kinds of Monte Carlo simulations. In this way, the accelerator assumes the load of generating these distributions, freeing the rest of the computational capacity to focus on the business logic and optimising the effectiveness, efficiency, and economy of the existing computational resources.

In addition, being able to generate probability distributions in such an efficient way increases the speed of sample generation. This speed is fundamental when establishing the accuracy of a Monte Carlo simulation. The low convergence speed of these Monte Carlo algorithms can thus be compensated by the high sampling speed. This compensation allows the Monte Carlo method to stop being a “last resort” method and become a complementary resource for tackling almost any numerical problem.

Additionally, using a high-quality entropy source such as Quside’s RPU guarantees the simulation’s quality and correct results. By integrating it, any potential artefacts that the pseudo-generator may introduce banish since we are using a pure entropy source to perform the simulations.

Whether it’s for on-premises deployments or cloud-based solutions, Quside RPU One caters to different requirements and improves financial activities exponentially. 

Quside RPU One benefits:

  • 10x faster in scenario simulation
  • 20x lower energy consumption

Finally, in anticipation of Basel 4, Quside RPU One, physical device and cloud, further enables financial institutions to adopt more rigorous risk assessment and management from improved simulations. The goal is to make the financial sector more secure in its risk- weighted assets and how they are calculated.

Accelerated randomness distribution

While traditional risk management tools conducting risk simulations using PRNG to be the basis for financial activities are struggling to keep up. At Quside, we have engineered a next-generation Randomness Processing Unit (RPU), anchored by our leading QRNG technology and randomness assurance tool: Metrology IP Core.

Quside has over a decade of research and scientific backing behind it and is now in our rapidly scaling stage. Our solutions provide measurable quality and high-speed entropy based on technology that has been tested in the most demanding scenarios, including experiments that led to the 2022 Nobel Prize-winning and peer-reviewed in the most exigent scientific publications.

We invite you to partner with us in extended application testing of Quside RPU One. 

Our latest product offering, RPU Cloud, is available on the leading platform, Amazon Web Services (AWS). The aim is for ease of deployment and agnostic compatibility, as RPU Cloud can be adapted to fit delivery specifications, such as the distribution model, by using client-tuned virtual machines. Finally, continuous improvements and iterations can also be rolled out, similar to a live service. Later, RPU Cloud will be available on other public platforms.

“At Quside, we work daily to improve our products and offer the customers the best version of each of them. Launching the RPU Cloud has been a very important achievement for us, since it brings us closer to our current customer base and also brings us closer to new potential users who already use Amazon Web Services» – Fernando de la Iglesia VP, Product at  Quside

Witness the next tier of speed of your simulated risk scenarios, optimising applications such VaR and CVaR.

Contact us for a discovery meeting here.