CASE STUDY

Finance & banking

Many financial calculations involve evaluating an enormous number of possible outcomes before identifying the best solution. Examples include optimising investment portfolios, modelling market risk, pricing complex financial instruments and managing liquidity across global financial networks.

Classical computers solve these problems using sophisticated algorithms that are highly effective but can become increasingly computationally demanding as the number of variables grows. Quantum computers are designed to tackle certain optimisation, simulation and statistical sampling problems in fundamentally different ways, potentially allowing some calculations to be performed more efficiently as the technology matures.

Quantum computing is not expected to replace existing banking systems. Instead, future financial platforms are likely to use hybrid computing, where conventional systems perform routine processing while quantum computers are applied only to specific computational tasks where they offer a measurable advantage. This measured, hybrid approach is widely regarded as the most realistic path for commercial adoption.

Financial institutions already employ advanced analytics, AI and high-performance computing to support decision-making. Quantum computing could become an additional tool for solving some of the industry's most computationally intensive problems.

Potential applications include:

Portfolio Optimisation Investment managers balance thousands of assets while considering expected returns, risk, regulation and investment constraints. Quantum optimisation algorithms could help evaluate more potential portfolio combinations, supporting improved investment strategies and faster rebalancing.

Risk Management Banks continually assess market, credit and operational risk using large-scale simulations. Quantum computing could accelerate aspects of these calculations, allowing institutions to explore a greater number of possible market scenarios and better understand rare but high-impact events.

Pricing Complex Financial Products Many financial products, including derivatives, require sophisticated mathematical models to estimate their value under changing market conditions. Quantum algorithms may improve some of the statistical methods used for pricing these products, particularly where repeated simulations dominate the computational cost.

Fraud Detection Financial institutions process billions of transactions every year. By combining quantum computing with AI, future systems may be able to identify unusual patterns across very large datasets, helping to detect fraudulent activity more quickly. While this remains an active area of research, it represents one of several potential applications of quantum-enhanced machine learning.

Treasury and Liquidity Management Banks constantly optimise cash flows across currencies, markets and payment systems. Quantum optimisation could support more efficient allocation of capital while maintaining regulatory requirements and operational resilience.

Insurance Insurance companies model uncertain future events ranging from floods to cyberattacks. Quantum computing could improve aspects of catastrophe modelling, portfolio optimisation and capital allocation by accelerating some underlying calculations.

Financial institutions face growing challenges from increasingly complex markets, larger datasets, evolving cyber threats and rising regulatory expectations. While today's computing technologies continue to advance, some financial calculations are becoming progressively more computationally intensive.

Quantum computing offers a promising long-term capability that could complement existing technologies for optimisation, simulation and data analysis. At the same time, organisations are already preparing for the future cybersecurity implications of quantum computing by adopting quantum-resistant cryptography. These two developments mean that quantum technologies are becoming strategically important for financial services well before large-scale commercial quantum computers become commonplace.

Many of the computational techniques developed for finance also have applications across other sectors. These include:

• Optimising energy trading and electricity markets.

• Managing large insurance and reinsurance portfolios.

• Detecting fraud across digital payment ecosystems.

• Improving supply chain finance and trade finance.

• Optimising large business operations and investment decisions.

• Supporting central bank economic modelling.

• Improving anti-money laundering (AML) analytics by identifying complex transaction patterns.