Introduction
The adoption of artificial intelligence methods in the financial services industry is opening the door to more robust data-driven decision processes, a better understanding of needs of their customers and, if used appropriately, will ultimately result in more resilient and trustworthy financial systems. However, there are also challenges such as algorithmic fairness, explainability, and the need for very high degrees of accuracy.
Explaining the science
Aims
This programme challenge brings together a multidisciplinary team of researchers, practitioners and regulators to promote the responsible adoption of AI techniques in the financial services industry. It offers trusted scholarly thought leadership in the area of finance.
The challenge span the following broad areas of activity:
- Addressing the key challenges of adopting machine learning techniques in the financial services industry by relying on transparent, reliable, and reproducible research
- Promotion of best practices for the use of machine learning tools for all areas of finance including the sell and buy side, risk management, data privacy, wholesale and retail banking
- Facilitating the swift transition of academic research outputs into practical solutions by creating collaborative projects with industry partners and a talent pool of researchers at the Turing
News
Uncertainty and risk – workshop
In March 2021, the Turing and the University of Oxford hosted a workshop to commemorate the centenary of publication of Frank Knight’s Risk, Uncertainty, and Profit and John Maynard Keynes’ A Treatise on Probability. View a recording of the workshop below.
Organisers
Dr Antoine Jacquier
Visiting Researcher
Professor Lukasz Szpruch
Programme Director for Finance and Economics