The Challenge
BBVA is one of the world’s largest financial institutions, with over €772 billion in assets and more than 77 million customers across 25+ countries. As a global bank operating at this scale, BBVA must run exceptionally complex and computationally intensive risk analyses to price financial products, manage exposure, and meet stringent regulatory requirements.
A significant portion of these workflows relies on Monte Carlo simulations to quantify market and credit risk. In areas such as credit valuation adjustment (CVA), derivative pricing, and stress testing, these simulations must account for thousands of possible future scenarios, making them resource-intensive, costly, and time-consuming.
Any reduction in runtime or computational load would have meaningful downstream impact on operational efficiency, financial-product pricing, and risk agility. BBVA partnered with Zapata to assess whether quantum or quantum-inspired methods could provide such improvements and help shape a long-term, data-driven quantum strategy.
Our Approach
Zapata and BBVA collaborated to design, analyze, and benchmark novel quantum approaches tailored to BBVA’s real-world credit-risk workflows. The work focused on two major fronts: (1) reducing the cost of Monte Carlo simulations, and (2) building a scalable evaluation and integration framework.
Monte Carlo: Zapata developed and evaluated variants of quantum amplitude estimation to determine whether they could reduce the computational burden of simulating expected exposures and credit-risk measures.
Framework: Beyond algorithm design, Zapata and BBVA established a workflow the bank can reuse as quantum technology matures for near-term insight and long-term strategic guidance. This included:
- Benchmarking quantum and classical algorithms on representative CVA workloads
- Identifying which subroutines are most “quantum-amenable”
- Creating a roadmap for integrating quantum hardware as it becomes available
Results & Impact
The collaboration produced a clear strategic understanding of quantum computing’s role in BBVA’s risk stack:
- The research demonstrated that quantum algorithms could reduce the sampling and computational requirements by orders of magnitude, accelerating the timeline for practical quantum utility in finance.
- The creation of a reusable methodology enabled BBVA to evaluate quantum utility across other financial modeling domains.
- BBVA now had a credible quantum adoption roadmap grounded in quantitative analysis to help guide its strategic direction.
Together, these results allowed BBVA to position itself at the forefront of quantum-enhanced financial modeling.
