Research
Our research develops practical insights and tools that apply our expertise and technology to prevailing industry challenges. We hope this is useful, and please reach out to us if you would like to explore these topics any further.
Machine learning and AI,
Digital art restoration has benefited from inpainting models to correct the degradation or missing sections of a painting. This work compares three current…
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Machine learning and AI, Quantitative algorithmic and financial engineering,
Abstract. Monitoring of loan performance and early identification of high-risk consumers aids prevention of loan defaults and is of interest to many banks and…
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Abstract. This paper presents an evolutionary algorithm (EA) capable of calculating the efficient frontier for a given portfolio. The objective of this paper is…
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Cloud engineering,
The financial industry has been cautious when moving functionality to the cloud, despite the obvious benefits. This is not surprising given how sensitive the…
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Quantitative algorithmic and financial engineering,
The automation of trading decisions has led to lower costs and greater liquidity but has the potential to amplify execution risks, turning otherwise manageable…
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Machine learning and AI, Quantitative algorithmic and financial engineering,
As deep learning enters the mainstream in financial services, interpretability is becoming an important issue; introducing models with hidden layers and complex…
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Technology re-platforming,
Ahead of the anticipated release of the final FRTB standards expected by the end of this year, we take a review industry feedback from the 42 respondents to…
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Technology re-platforming,
Chris Sawyer comments on OpenJDK and Oracle JDK, the importance of test-driven development and UI colour palettes.
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