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The huge processing power of AI and machine learning is transforming financial services and improve the analysis of risk.
AI research at UCL seeks to understand the biases and risks that can arise through the application of new financial technologies and to develop novel financial tools for a variety of financial services contexts.
Find out how UCL researchers are using AI to solve finance and risk issues below.
Finance
Computational contracts
Computable Contracts aim to transform the activity of commercial contracting. Currently, contracts are static documents that are often disconnected from other business activities. In the future, they will be more interactive and integrated expressions of the intentions of the parties that are understandable by computers as well as by humans, and that have significantly greater levels of digital connectivity.
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Financial applications of AI
The Financial Mathematics Group conduct research on asset pricing and hedging, financial risk management, computational methods for finance and insurance, stochastic (partial) differential equations, control theory and applications, algorithmic finance, filtrations and information modelling, probabilistic numerical methods, rough path theory, statistical inference and machine learning, and research on heavy-tailed processes.
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Smart contracts
Smart Contracts utilise computer technology to automate an agreement. Research on Smart Contracts at UCL currently focuses on legal drafting and legal specification languages (Computable Contracts), privacy, semantics and analysis, Smart Derivatives Contracts, Smart Contract Templates, smart contract code and associated system architectures.
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Statistics and data
Noisy Data Streams
The Alan Turing Institute conducts research in data science and artificial intelligence to tackle some of the biggest challenges in science, society and the economy. UCL is one of five academic partners for the Institute. The Turing Analysing Noisy Data Streams project (under the data-centric engineering programme) aims to develop accurate, efficient, and robust statistical methodologies for analysing complex and noisy streams of data with focused financial applications.
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Stochastic Modelling of Complex Systems
Stochastic Modelling of Complex Systems research covers the development of generic stochastic models and the investigation of their properties, as well as modelling and inference for applications in a range of physical and biological sciences.