Intelligent Solutions
for Complex Problems
Cambridge Inference provides cutting-edge mathematical modeling, machine learning, and optimization solutions for the banking sector.
About Cambridge Inference
Our Mission
Cambridge Inference was founded to bridge the gap between academic mathematical research and real-world business problems. We deliver practical, scalable solutions using advanced mathematical techniques, machine learning, and optimization algorithms.
Our team combines academic excellence with industry experience to tackle the most challenging problems across multiple sectors, providing competitive advantages through data-driven solutions.
Our Approach
We begin by developing a deep understanding of each client's specific challenges, focusing on the practical constraints and operational realities that traditional approaches often overlook. This allows us to design solutions that work in practice, not just in theory.
What sets us apart is our ability to translate complex mathematical concepts into tangible business value. We measure success not by theoretical elegance alone, but by the real-world impact our solutions deliver—whether that's cost reduction, improved decision-making, or new strategic capabilities for our clients.
Our Services
We develop tailored mathematical models that capture the essential dynamics of your business problems, enabling quantitative analysis, statistical inference, and optimization.
Our machine learning solutions range from Bayesian statistical inference to deep learning, helping you uncover patterns, make predictions, and automate decision processes.
We implement state-of-the-art optimization algorithms to help you maximize efficiency, minimize costs, and make effective decisions under constraints.
Our Leadership Team
Cambridge University Professor of Astrophysics, with over 400 publications and 80,000 citations, ranking among the UK's most cited physicists. Has applied his sophisticated knowledge of statistical methods to solve complex problems in fields including financial markets, geological resource exploration, and defence applications.
Specialist in Bayesian inference and machine learning with applications in astrophysics, quantitative finance and AI security. Former Cambridge University research fellow with doctoral work in gravitational wave astronomy and precision pulsar timing.
Theoretical physicist turned data scientist. Leverages his expertise in mathematics, optimisation methods, and machine learning to tackle complex challenges in finance and business.
Case Studies
Explore how we've helped organisations across different industries solve their most challenging problems with our innovative mathematical approaches.
We developed a mathematically proven optimal solution to the complex challenge of collateral allocation, enabling banks to efficiently assign assets against multiple obligations.
Our proprietary approach significantly outperforms existing industry methods, delivering substantial cost savings while ensuring full compliance with all regulatory and counterparty constraints.
Key Results:
- Provably optimal solutions
- Significant annual savings compared to existing methods
- Efficient processing within operational timeframes
- Robust stress-testing capabilities
Get in Touch
Interested in working with us?
Contact our team to discuss your challenges.