Applying emerging quantum technology to financial problemsDownload the report
Financial services has a history of successfully applying physics to help solve its thorniest problems. The Black-Scholes-Merton model, for example, uses the concept of Brownian motion to price financial instruments – like European call options – over time.
Applying emerging quantum technology to financial problems—particularly those dealing with uncertainty and constrained optimization—should also prove hugely advantageous for first movers. Imagine being able to make calculations that reveal dynamic arbitrage possibilities that competitors are unable to see. Beyond that, greater compliance, employing behavioral data to enhance customer engagement, and faster reaction to market volatility are some of the specific benefits we expect quantum computing to deliver.
What gives quantum computing this enormous advantage? The solution space of a quantum computer is orders of magnitude larger than traditional computers—even immensely powerful ones. That’s because doubling the power of a classical computer requires about double the number of transistors working on a problem. The power of a quantum computer can be approximately doubled each time only one qubit is added.
While broad commercial applications may remain several years away, quantum computing is expected to produce breakthrough products and services likely to successfully solve very specific business problems within three-to-five years.
Quantum computing can also enable financial services organizations to re-engineer operational processes, such as:
– Front-office and back-office decisions on client management for “know your customer,” credit origination, and onboarding,
– Treasury management, trading and asset management,
– Business optimization, including risk management and compliance.
Quantum computing’s specific use cases for financial services can be classified into three main categories: targeting and prediction, trading optimization, and risk profiling.
We explore potential use cases in each of these categories, providing examples that apply to three main industries in financial services: banking, financial markets, and insurance.
Classical computers limit the potential of machine-learning to solve specific financial services problems, whereas quantum computing promises higher quality solutions.
Because combinatorial optimization problems in trading and portfolio management scale exponentially, quantum computers have the potential to find faster, more cost-effective and better-tailored solutions than classical machines.