by Martina Gschwendtner, Nicole Morgan, and Henning Soller
Quantum computing is coming.
Many experts predict that a full-scale fault-tolerant quantum computer could become reality by 2035. But significant technical developments have already been under way. This means financial services organizations can reap significant value well before fault-tolerant quantum computers come online by learning, building business capabilities, and aligning business processes to accelerate integration when fault-tolerant quantum computers become available. Machines can be used for efforts such as pricing derivatives or creating inputs to optimize portfolios.
By the time a fault-tolerant quantum computer is available, we estimate that the use cases in finance could create $622 billion in value (exhibit).1
This value comes from improving existing processes and from use cases that could change the financial system.
In this post, we offer an overview of potential quantum computing use cases across business units in financial services: corporate banking, risk and cybersecurity, retail banking, payments, wealth management, investment banking, and operations and finance. Using market analysis and expert interviews, we have identified the possible impact of quantum-technology use cases for each business unit and the value at stake.
Quantum-computing use cases in finance
Potential use cases in finance go beyond commonly mentioned ones such as portfolio optimization and include a few that could be revolutionary.
The most significant applications of quantum computing are in corporate banking because of the high monetary value at stake and the numerous complex use cases in areas such as trade finance.
Consider collateral optimization for use cases such as securities lending, which involves cross-optimizing multiple sets of variables. Optimization problems with increasing numbers of variables and constraints become increasingly complex. Certain subparts of the optimization can be outsourced to a quantum computer, solved with higher accuracy, and then brought back to the overall calculation.2 This approach can similarly be used to estimate the probability of default, which consists of many optimization variables, before making a lending decision.
Quantum computing assists in making significant decisions and even allows for automated decisions in real time. This comes from the technology’s inherent ability to conduct calculations with a much larger set of boundary conditions and improve the accuracy of decisions. For example, the technology could support holistic simulations of liquidity.
Quantum computing and quantum networking also open new possibilities for different asset classes and the execution of transaction banking. Fault-tolerant smart contracts, nontangible assets, and secure communication could all become possible and exceed the speed and security of blockchain solutions.
Risk and cybersecurity
Risk and cybersecurity center on concerns such as accurate and fast fraud detection as well as defense against quantum computers’ ability to decrypt data.
Risk is one of the most complex business units in banks, because it involves identifying, mitigating, and reporting risks across other units. With quantum computing, the regulatory reporting associated with risk will likely undergo a massive shift.
Quantum machine learning can allow decision makers to consider a broader set of variables and assets when simulating risks, reducing the cost of risk and facilitating larger deals with even higher margins.
For fraud detection, new types of payment and transactions can minimize incidents. While experts currently believe quantum computing can only marginally improve rule-based heuristics for fraud detection, more variables can boost the accuracy of fraud-detection algorithms.
A full-scale fault-tolerant quantum computer would be able to decrypt currently available cryptographic protocols. Even data that is currently safe can be harvested by bad actors to decrypt later, when quantum technologies make that possible.
Post-quantum cryptography (PQC) and quantum key distribution (QKD) are the leading approaches to making data quantum-safe. PQC algorithms are classical, quantum-resistant algorithms consisting of cryptographic problems that are computationally difficult. QKD uses quantum properties to establish a secure communication channel between two parties. Any attempt to eavesdrop or intercept the exchange of encryption keys would be detected, causing the secret keys to be discarded.3
In the short term, PQC is more practical than QKD because it is algorithm-based and doesn’t require specialized hardware. As quantum technology evolves, security updates will likely involve incorporating PQC first because it is compatible with current infrastructure. In the long term, QKD will become more practical as special-purpose hardware becomes available.
Quantum computing’s use cases in retail banking are largely similar to those in corporate banking, especially for high-net-worth individuals. However, most of the challenges would be simpler for retail clients because the individual assets and volumes are smaller.
Two major use cases are credit-decision algorithms and collateral optimization, both of which could benefit from greater accuracy. The former can harness quantum computing to consider a broader variety of relevant factors. The latter can incorporate more values and data types as boundary conditions of the optimization problem. As the problems on the retail banking side are typically limited in complexity and number of variables due to the inherent structure, the current belief is that there is not much gain in this business unit. To a smaller extent, portfolio quantum computing could also boost portfolio optimization.
Quantum states would offer security and significantly faster payments compared with the blockchain. Specifically, quantum payments would eliminate the ongoing issue of money laundering on the blockchain.
Quantum states represent truly nonfalsifiable money, since they cannot be cloned. One critical enabler of quantum payments we expect to be forthcoming is the hardware required for QKD.
In the future, quantum money, which is based on QKD protocols, could revolutionize security for intra- and interbank trades. Stable implementation of quantum money will effectively transform the banking ecosystem.
The uses and challenges of quantum computing in wealth management are similar to those in investment banking. The main difference is that a wealth manager may have less understanding of the assets under management than do asset managers of investment banks.
Looking ahead, quantum technologies could facilitate the management of nonphysical assets by encoding contracts in quantum states. This is faster, more secure, and more sustainable compared with the current blockchain solutions, which require a mining process.
Quantum-computing use cases in investment banking can be most readily found in portfolio optimization and derivatives pricing.
With quantum computing, investment banking teams could create full digital twins of a bank’s positions that they could use to simulate various macroconditions and pathways. Teams could also perform granular simulations to see how different scenarios would affect every asset of a bank and to create quantum models of markets. The result could be optimized capital allocation with respect to collateral and assets.
Derivative pricing and high-frequency trading could let users account for more variables and examine the assets underlying the derivate. Compute-heavy assets could be analyzed by a quantum computer and then fed back into the main algorithm.
In the short term, quantum-computing capabilities such as random-number generation can boost the accuracy and speed of classical Monte Carlo simulations. In the long term, banks could implement quantum Monte Carlo algorithms, which can quadratically speed up existing classical versions.
In the future, smart contracts and institutional trades facilitated by quantum states can produce faster, more-secure, and less-energy-intensive transactions, similar to situations with the corresponding asset classes in wealth management.
Operations and finance
Quantum computing lends itself to solving problems of natural-language processing because of the large amount of data and number of boundary conditions inherent in language.
Machine learning currently matches supply with demand in call-center operations and uses data to optimize scheduling. Quantum computing could help categorize tasks more accurately and match tasks to the appropriate operators by complexity and sophistication, optimizing the use of the workforce. Over time, high-quality data and advances in data privacy can help quantum computing support even the complex operations of large call centers.
In finance functions, banks could use quantum computing to optimize finances and accounting and to augment insights from human experts. This would make any resulting decisions more accurate. Of course, support from quantum computing cannot replace legal expertise in tax law, particularly in ambiguous cases. But over time, more-consistent and higher-quality data could help quantum computing better augment experts’ work.
Foundational moves to tap into the value of quantum computing
The value of quantum technologies in finance may be significant, but realizing that value is a long game. Three broad moves can set financial institutions up to make use of quantum technologies effectively and sustainably in the coming years:
- Start now. If a company has not yet started building quantum capabilities, the best time to start is now. In particular, decision makers can continually identify relevant use cases and work closely with senior executives to generate sponsorship and sustained funding that reflects the significance of the investment. Organizations need to start preparing now for the quantum threat to cybersecurity.
- Begin capturing value. Financial institutions could start experimenting with quantum use cases now for uses such as derivative pricing or seeding Monte Carlo simulations. Experimenting doesn’t always mean working directly with quantum technologies. Insights from quantum technologies can improve many classical solutions for near-term economic advantage. Decision makers also shouldn’t discount the first-mover advantage. Getting into the practice of capturing value from quantum technologies is good preparation for getting into that first-mover position.
- Involve every business unit. Value often comes from unexpected sources, and this may yet be true for quantum technologies. Every business unit would benefit from being considered and involved in discussions about possible quantum technology use cases.
Significant technological breakthroughs in quantum technologies will precede the ultimate commercial breakthrough of financial institutions fully using quantum technologies. Every business unit could produce billions in value when the time comes. But companies can start tapping into the value of quantum technology now and, crucially, get into winning positions well before their competitors. The time to build capabilities is now.
Martina Gschwendtner is a consultant in McKinsey’s Munich office, Nicole Morgan is a consultant in the London office, and Henning Soller is a partner in the Frankfurt office.
The authors wish to thank Sven Blumberg, Holger Harreis, Reinhard Höll, and Jared Moon for their contributions to this post.
1 These numbers are based on the potential quantum impact on revenue streams and costs across finance business units.
2 Simon C. Benjamin et al., “Hybrid quantum-classical algorithms and quantum error mitigation,” Journal of the Physical Society of Japan, March 2021, Volume 90, Number 3.
3 For more on QKD, see Leonardo Banchi et al., “Advances in quantum cryptography,” Advances in Optics and Photonics, 2020, Volume 12, Number 4.