How Quantum Computing Transforms Financial Modeling & Risk

Before AI, quantum computing was the “next big thing” in the tech industry. Comparisons that allowed people to digest just how powerful quantum computers can be compared to conventional electronic computers shook the market. The most famous one is that a quantum computer of sufficient power might break through a 2048 RSA encryption (roughly a 20+ character, strong password) in hours, compared to conventional computers that may take billions of years brute forcing all the combinations.

A quantum computer’s ability to process millions of possibilities in parallel may only have narrow applicability for now, but even in its nascent form, it may have significant implications for financial modeling and risk management. Quantum-enabled fintech software development services are emerging and gaining traction among market leaders.

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The overarching benefits of quantum computing in finance

Before we dive into quantum computing’s impact on specific financial modeling and risk management use cases, scenarios, and possible impact, it is useful to zoom out and look at three overarching benefits. These benefits “shape” the impact of quantum computing in finance. It’s useful to assess these benefits in comparison to conventional computing resources and cost.

Scope and Speed: Quantum computers can process far more data simultaneously and quickly than conventional computers, which are limited by their binary nature. They can process possibilities sequentially (one computation at a time) and only as many in parallel as the number of cores of a processor allows. In contrast, quantum computers can run exponentially more possibilities simultaneously and use interference to identify the right answers.

Computing Efficiency: Once stable, quantum computers may need far fewer resources (electricity and hardware) than conventional computers for the same level of computation power. They are also far easier to scale.

Richer and More In-Depth Evaluations: The efficiency and parallel processing of numerous variables and possibilities allow for richer and more in-depth evaluations of a wider range of scenarios than conventional computers allow.

These capabilities allow quantum computing to have a powerful impact on financial modeling and risk management.

Quantum computing’s impact on financial modeling

Forecasting is the primary goal of financial modeling, though not the only one. Quantum computing can supercharge the forecasting capabilities of financial models, allowing them to forecast complex scenarios in real-time and generate more granular insights. The enhanced accuracy and scope of financial modeling can help financial institutions and relevant stakeholders make more timely and informed decisions. It can also power advanced automated financial decision-making based on pre-determined financial modeling thresholds.

Quantum computers’ impact on financial modeling is already obvious from certain pilot projects and experimental studies.

Pilot projects

Looking into a few pilot projects can help with the assessment of the existing real-world impact of quantum computing on financial modeling.

Corporate Bond Trading: HSBC used IBM’s quantum computers to optimize algorithmic bond trading in the European market. It wasn’t a pure implementation, but even a combination of classical models run on quantum hardware (not fully stable) resulted in a 34% improvement over the same models running on conventional hardware. This indicates what could be achieved with quantum hardware still in its infancy.

Portfolio optimization: JPMorgan and Chase are already working on leveraging quantum computing for portfolio optimization. It has developed its own version of a quantum algorithm – Harrow-Hassidim-Lloyd (HHL) algorithm (designed to run on stable quantum hardware) that may be executed on existing quantum computers for advanced portfolio optimization.

Promising domains

While quantum computing has the capability to transform financial modeling, it will most likely happen in stages. Some domains and financial modeling scenarios are more poised than others (including the following).

It’s also worth noting that the Monte Carlo simulation that is designed to predict possible outcomes in scenarios riddled with random variables is a major topic of quantum computing research. Quantum-enabled Monte-Carlo simulation can elevate various financial modeling scenarios to new heights.

Derivative Pricing: While many algorithms and simulations (including Monte Carlo) exist for derivative pricing, it becomes slower and more computationally expensive, the more complex the derivative is, and the more variables that influence its pricing. Quantum computing is expected to solve this dimensionality problem and lead to faster, more efficient, and more accurate derivative pricing by reconciling far more variables than we currently can.

Personalization: Quantum computing is expected to enable real-time processing of massive behavioral datasets and serve personalized recommendations for financial products and services to consumers to maximize conversions.

Market Forecasting: By analyzing the impact of a wide range of macro and micro factors on specific markets, their subsets, products within the market, and stakeholder behavior, quantum computing can significantly enhance the practice of market forecasting.

Stress Testing: Quantum-augmented economic stress-testing would allow for more comprehensive stress simulations, covering more stakeholders and variables, allowing for the identification of narrower thresholds and more aggressive decision-making.

Many of these financial modeling domains require “nested” Monte Carlo and other simulations, which are extremely expensive and relatively slow with conventional hardware.

How quantum computing is changing risk management

Risk management relies upon accurately predicting certain probabilities. The probability of a borrower defaulting on their loan, of an application being fraudulent, or an investment falling below acceptable thresholds in certain market conditions. Quantum computing is expected to increase the accuracy of predicting these probabilities by a significant margin, leading to better risk management with fewer dedicated resources.

While it’s expected to have an impact on several risk management domains and use cases, the most prominent ones include:

Fraud detection

Quantum methods and computing resources are already being applied to fraud detection, one example being Deloitte Italy’s quantum machine learning solution for fraud detection, which offered more precision despite having fewer neural network parameters compared to its classical counterpart.

The idea is that quantum-enabled fraud detection will be able to account for more fraud signals in real time and run more complex scenarios for fraud probabilities, leading to more accurate and precise results and fewer false positives and negatives. Both can have disastrous consequences in fraud detection.

Value-at-Risk

Value-at-risk calculation is computationally demanding and time-intensive, especially for more complex portfolios and assets that interact with and are influenced by several micro and macro variables. It’s also impossible to reconcile all variables and situations simultaneously, resulting in multiple simulations and calculations, further complicating the process.

Quantum computing is expected to help on both fronts – allowing for more complex value-at-risk calculations that current statistical models and computers cannot support, and do so more efficiently.

Credit scoring

Credit scoring is another area where quantum computing interacts with machine learning. Quantum-enhanced ML models are significantly faster to train and consequently less resource-intensive. They may also produce similar accuracy in credit scoring despite far fewer epochs, i.e., one full pass through the whole dataset.

This indicates that quantum-enhanced models can learn relevant patterns from a dataset far more efficiently than conventionally run models. Faster, more resource-efficient, and more accurate credit scoring can have an enormous impact on a financial institution’s ability to evaluate potential candidates for loans and other financial products.

Current limitations of quantum computing

Despite the enormous potential of quantum computing in financial modeling and risk management, and several other financial and other industry domains, we mostly see pilot projects and experiments, with few real-world implementations. This is because of three quantum computing bottlenecks.

Hardware: Quantum computers are currently incredibly expensive and sensitive. For now, most computers and quantum hardware are owned by the government and academia. This prevents financial service providers from starting to experiment with quantum computing, and only a few have made any significant headway.

Encoding: The data that runs well on classical computers has to be encoded for quantum computers and algorithms. This data conversion is a huge bottleneck, especially considering that the main strength of quantum computing is to process vast amounts of data simultaneously.

Algorithm: There are relatively few quantum algorithms proposed at all, and not all of them apply to financial modeling and risk management scenarios.

However, these bottlenecks and limitations are a temporary hurdle, and we may see significant strides in quantum usage in the financial industry in the coming decade.

Conclusion: Fintechs’ next steps in a quantum revolution

Artificial intelligence is getting most of the limelight (and resources) from fintechs and conventional financial institutions, leaving quantum computing in the shadows. But this will change as fintechs start waking up to the enormous potential that quantum computing and techniques are offering, not just for financial modeling and risk management but also for other domains.

A smart approach for fintechs is to start working with an innovation-focused fintech software development company like 10Pearls that are experienced in working on the cutting-edge of technologies. From identifying optimal use cases to developing long-term quantum strategies, an early start on quantum computing can give fintechs a significant competitive edge.