Quantum Technologies Key Strategies and Opportunities for Financial Services Leaders 2025

Early case studies (non-exhaustive) Quantum computing Why it’s important Benefit gained Quantum computing delivers superior performance for complex computations, enabling breakthroughs in optimization and data analysis. Catalyst for transformation Quantum computing acts as a transformative force, enabling financial services firms to reimagine traditional processes. By harnessing quantum algorithms, organizations can explore new frontiers in solving business challenges and anticipating risks. How to approach integration Strategic exploration Identify key areas where quantum computing can add strategic value and create a comprehensive plan for responsible integration into business operations. Active and multifaceted engagement Initiate pilot projects, cultivate partnerships, invest in training, prioritize quantum use cases and stay informed on advancements to drive innovation and adoption.With its unmatched computational power, quantum computing is poised to transform industries and enable groundbreaking applications. Strategic adoption can benefit financial services organizations in multiple ways.CASE STUDY 1 Financial crash estimation within a network of small and medium-sized enterprises Understanding and predicting financial crashes is vital for maintaining global economic stability. A key problem in financial mathematics is identifying vulnerabilities in financial institution networks to prevent crises that could impact businesses and markets.14 Turkish bank Yapı Kredi has taken a significant step towards addressing this issue by developing an innovative approach to estimating financial risks. As part of an R&D initiative, Yapı Kredi created a model to identify potential failure points in its network of small- and medium-sized enterprises (SMEs). Identifying potential failure points is crucial to avoiding potential domino effects (wherein a single delayed payment may initiate a chain reaction of financial distress among interconnected enterprises, ultimately resulting in a cascading failure across the entire financial network). Analysing these intricate networks using classical computing, however, is difficult and time-consuming. To overcome this, the R&D team used quantum computing technology from D-Wave, which allowed them to efficiently explore thousands of possible scenarios and pinpoint businesses at risk of financial distress. This provided valuable insights for credit loan departments, helping the bank identify hidden risks not yet reflected in customers’ financials and make better decisions.15 “Risk management is one of the most critical components of banking. We applied our model to a real-life scenario covering 4,297 out of a network of over 600,000 corporate clients. An analysis that would traditionally take years to compute was completed in just seven seconds thanks to the technology we developed. Our goal is to carry our clients into the future with a more robust financial infrastructure and to establish a model that will shape the industry.” – Gökhan Özdinç, Executive Vice President, Technology, Data and Process Management, Yapı Kredi. Importantly, all data was anonymized to comply with regulations. Looking ahead, Yapı Kredi plans to scale this approach to its entire SME network and expand its use to other areas of the organization. This experiment demonstrates how cutting-edge technology can help financial institutions protect businesses, mitigate risks and ensure long- term stability. CASE STUDY 2 Advantages of financial fraud detection using quantum machine learning Fraud detection poses a significant challenge for financial institutions, compelling banks to undertake a variety of activities, including real-time transaction monitoring, advanced algorithms and biometric verification. Traditional machine learning algorithms, however, often struggle to accurately identify fraud patterns, limiting their effectiveness. To address this, Italian bank Intesa Sanpaolo explored the use of quantum computing to improve fraud detection. The bank conducted a study using quantum machine learning, specifically variational quantum circuit (VQC)-based classifiers. By analysing a dataset of 500,000 transactions, the R&D team reduced the data complexity to match current quantum hardware capabilities. Using IBM’s quantum tools, the quantum model outperformed traditional methods in identifying fraud and achieving better accuracy and efficiency with fewer data features. This approach also introduced transfer learning, enabling models trained on one dataset to be applied to others, cultivating collaboration and innovation across the financial sector. Due to quantum hardware limitations, the study run by Intesa Sanpaolo used a phased approach starting with quantum-inspired (classical hardware) solutions, followed by the integration of quantum tools with existing systems and concluding with full adoption of quantum- based workflows when available. Compliance was ensured through anonymized datasets and privacy-by-design principles, aligning with regulatory standards and organizational security practices. This initiative highlights the potential of quantum computing to revolutionize fraud detection. By improving accuracy and enabling collaboration through open-source platforms, banks can strengthen anti-fraud measures and enhance global financial security. As quantum technology advances, its scalability will unlock even greater benefits for the industry.16FIGURE 3 Quantum Technologies: Key Strategies and Opportunities for Financial Services Leaders 9