FAIR: Framework for responsible adoption of Artificial Intelligence in the financial seRvices industry
Lead Research Organisation:
The Alan Turing Institute
Department Name: Research
Abstract
AI technologies have the potential to unlock significant growth for the UK financial services sector through novel personalised products and services, improved cost-efficiency, increased consumer confidence, and more effective management of financial, systemic, and security risks. However, there are currently significant barriers to adoption of these technologies, which stem from a capability deficit in translating high-level principles (of which there is an abundance) concerning trustworthy design, development and deployment of AI technologies ("trustworthy AI"), including safety, fairness, privacy-awareness, security, transparency, accountability, robustness and resilience, to concrete engineering, governance, and commercial practice.
In developing an actionable framework for trustworthy AI, the major research challenge that needs to be overcome lies in resolving the tensions and tradeoffs which inevitably arise between all these aspects when considering specific application settings.For example, reducing systemic risk may require data sharing that creates security risks; testing algorithms for fairness may require gathering more sensitive personal data; increasing the accuracy of predictive models may pose threats to fair treatment of customers; improved transparency may open systems up to being "gamed" by adversarial actors, creating vulnerabilities to system-wide risks.
This comes with a business challenge to match. Financial service providers that are adopting AI approaches will experience a profound transformation in key areas of business as customer engagement, risk, decisioning, compliance and other functions transition to largely data-driven and algorithmically mediated processes that involve less and less human oversight. Yet, adapting current innovation, governance, partnership and stakeholder relation management practice in response to these changes can only be successfully achieved once assurances can be confidently given regarding the trustworthiness of target AI applications.
Our research hypothesis is based on recognising the close interplay between these research and business challenges: Notions of trustworthiness in AI can only be operationalised sufficiently to provide necessary assurances in a concrete business setting that generates specific requirements to drive fundamental research into practical solutions, with solutions which balance all of these potentially conflicting requirements simultaneously.
Recognising the importance of close industry-academia collaboration to enable responsible innovation in this area, the partnership will embark on a systematic programme of industrially-driven interdisciplinary research, building on the strength of the existing Turing-HSBC partnership.
It will achieve a step change in terms of the ability of financial service providers to enable trustworthy data-driven decision making while enhancing their resilience, accountability and operational robustness using AI by improving our understanding of sequential data-driven decision making, privacy- and security- enhancing technologies, methods to balance ethical, commercial, and regulatory requirements, the connection between micro- and macro-level risk, validation and certification methods for AI models, and synthetic data generation.
To help drive innovation across the industry in a safe way which will help establish the appropriate regulatory and governance framework, and a common "sandbox" environment to enable experimentation with emerging solutions and to test their viability in a real-world business context. This will also provide the cornerstone for impact anticipation and continual stakeholder engagement in the spirit of responsible research and innovation.
In developing an actionable framework for trustworthy AI, the major research challenge that needs to be overcome lies in resolving the tensions and tradeoffs which inevitably arise between all these aspects when considering specific application settings.For example, reducing systemic risk may require data sharing that creates security risks; testing algorithms for fairness may require gathering more sensitive personal data; increasing the accuracy of predictive models may pose threats to fair treatment of customers; improved transparency may open systems up to being "gamed" by adversarial actors, creating vulnerabilities to system-wide risks.
This comes with a business challenge to match. Financial service providers that are adopting AI approaches will experience a profound transformation in key areas of business as customer engagement, risk, decisioning, compliance and other functions transition to largely data-driven and algorithmically mediated processes that involve less and less human oversight. Yet, adapting current innovation, governance, partnership and stakeholder relation management practice in response to these changes can only be successfully achieved once assurances can be confidently given regarding the trustworthiness of target AI applications.
Our research hypothesis is based on recognising the close interplay between these research and business challenges: Notions of trustworthiness in AI can only be operationalised sufficiently to provide necessary assurances in a concrete business setting that generates specific requirements to drive fundamental research into practical solutions, with solutions which balance all of these potentially conflicting requirements simultaneously.
Recognising the importance of close industry-academia collaboration to enable responsible innovation in this area, the partnership will embark on a systematic programme of industrially-driven interdisciplinary research, building on the strength of the existing Turing-HSBC partnership.
It will achieve a step change in terms of the ability of financial service providers to enable trustworthy data-driven decision making while enhancing their resilience, accountability and operational robustness using AI by improving our understanding of sequential data-driven decision making, privacy- and security- enhancing technologies, methods to balance ethical, commercial, and regulatory requirements, the connection between micro- and macro-level risk, validation and certification methods for AI models, and synthetic data generation.
To help drive innovation across the industry in a safe way which will help establish the appropriate regulatory and governance framework, and a common "sandbox" environment to enable experimentation with emerging solutions and to test their viability in a real-world business context. This will also provide the cornerstone for impact anticipation and continual stakeholder engagement in the spirit of responsible research and innovation.
Organisations
- The Alan Turing Institute (Lead Research Organisation)
- HSBC Bank Plc (Collaboration)
- Mozilla (Collaboration)
- Microsoft Corporation (Collaboration)
- Privitar (Collaboration, Project Partner)
- Quantexa LTD (Collaboration)
- Accenture (Collaboration)
- GOFCoE - Global Open Finance Centre (Project Partner)
- Microsoft (United Kingdom) (Project Partner)
- Quantexa (Project Partner)
- HSBC Holdings (Project Partner)
- Mozilla Foundation (Project Partner)
- Accenture (United Kingdom) (Project Partner)
Publications
Abroshan M
(2022)
Counterfactual Fairness in Synthetic Data Generation
Al-Khateeb H
(2022)
Pivot Attack Classification for Cyber Threat Intelligence
in Journal of Information Security and Cybercrimes Research
Aminian G
(2023)
Information-Theoretic Characterizations of Generalization Error for the Gibbs Algorithm
in IEEE Transactions on Information Theory
Bennett S
(2022)
Lead-lag detection and network clustering for multivariate time series with an application to the US equity market
in Machine Learning
Benussi E
(2022)
Individual Fairness Guarantees for Neural Networks
Bertucci L
(2022)
Deep Learning in Finance: From Implementation to Regulation
in SSRN Electronic Journal
Cao H
(2021)
Identifiability in inverse reinforcement learning
Cohen S
(2023)
Estimating risks of European option books using neural stochastic differential equation market models
in Journal of Computational Finance
Cohen S
(2023)
Inefficiency of CFMs: Hedging Perspective and Agent-Based Simulations
in SSRN Electronic Journal
Gholamali Aminian
(2022)
Semi-Counterfactual Risk Minimization Via Neural Networks
Gholamali Aminian
(2022)
Information-theoretic Characterizations of Generalization Error for the Gibbs Algorithm
Gourdeau P
(2022)
When are Local Queries Useful for Robust Learning?
Gourdeau P
(2022)
When are Local Queries Useful for Robust Learning?
Houssiau F
(2022)
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data
Houssiau F
(2022)
On the difficulty of achieving Differential Privacy in practice: user-level guarantees in aggregate location data
in Nature Communications
Jordon J
(2022)
Synthetic Data - what, why and how?
Khajehnejad A
(2022)
CrossWalk: Fairness-Enhanced Node Representation Learning
Kwiatkowska M
(2022)
Robustness Guarantees for Bayesian Neural Networks
Liu L
(2023)
Polyak-Lojasiewicz inequality on the space of measures and convergence of mean-field birth-death processes
in Applied Mathematics & Optimization
Maddock S
(2022)
Federated Boosted Decision Trees with Differential Privacy
Malfa E
(2022)
The King Is Naked: On the Notion of Robustness for Natural Language Processing
in Proceedings of the AAAI Conference on Artificial Intelligence
Marques R
(2022)
APIVADS: A Novel Privacy-Preserving Pivot Attack Detection Scheme Based on Statistical Pattern Recognition
in IEEE Transactions on Information Forensics and Security
Radanliev P
(2022)
Super-forecasting the 'technological singularity' risks from artificial intelligence.
in Evolving systems
Radanliev P
(2022)
Forecasts on Future Evolution of Artificial Intelligence and Intelligent Systems
in IEEE Access
Samuel Cohen
(2022)
Estimating risks of option books using neural-SDE market models
Samuel Cohen
(2022)
Hedging option books using neural-SDE market models
Samuel Cohen
(2022)
Neural Q-learning for solving elliptic PDEs
Shamsabadi A
(2023)
Confidential-PROFITT: Confidential PROof of FaIr Training of Trees
Shaukat A
(2022)
A Novel Privacy Paradigm for Improving Serial Data Privacy.
in Sensors (Basel, Switzerland)
Wicker M
(2023)
Robust Explanation Constraints for Neural Networks
| Description | Network Stochastic Processes and Time Series (NeST) |
| Amount | £6,451,752 (GBP) |
| Funding ID | EP/X002195/1 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2023 |
| End | 12/2028 |
| Description | Privacy Risk Assessment Methodology |
| Amount | £49,767 (GBP) |
| Funding ID | MC_PC_21030 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 01/2022 |
| End | 08/2022 |
| Description | Accenture |
| Organisation | Accenture |
| Country | Ireland |
| Sector | Private |
| PI Contribution | We are bringing research expertise and research leadership both in the development and delivery of our research projects. We have also provided operational expertise in the setting up and delivery of projects and in governance activity. Additional value has been added via engagement activity and our internship network which has placed PHD students within Accenture. |
| Collaborator Contribution | Accenture is bringing business leadership and insight. |
| Impact | Individual Fairness Guarantees for Neural Networks. Journal article / Review https://arxiv.org/abs/2205.05763 Robust Explanation Constraints for Neural Networks Journal article / Review [2212.08507] Robust Explanation Constraints for Neural Networks (arxiv.org) |
| Start Year | 2020 |
| Description | HSBC (Industry partner) |
| Organisation | HSBC Bank plc |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | We are bringing research expertise and research leadership both in the development and delivery of our research projects. We have also provided operational expertise in the setting up and delivery of the partnership, including governance. Additional value has been added via engagement activity and our internship network which has placed PHD students within HSBC. |
| Collaborator Contribution | HSBC is bringing research expertise and business leadership. They are bringing live use cases and data sets as well as hardware and other means to facilitate date access including HSBC laptops. |
| Impact | No research outcomes yet. |
| Start Year | 2016 |
| Description | Microsoft |
| Organisation | Microsoft Corporation |
| Country | United States |
| Sector | Public |
| PI Contribution | N/A |
| Collaborator Contribution | Microsoft are providing cloud credits - $100k per annum to be drawn down from. |
| Impact | N/A |
| Start Year | 2021 |
| Description | Mozilla Foundation |
| Organisation | Mozilla |
| Country | Global |
| Sector | Charity/Non Profit |
| PI Contribution | N/A |
| Collaborator Contribution | Estimated value of in-kind support to be £150k over 5 years in the form of advisory support and participation in engagement activity |
| Impact | N/A currently |
| Start Year | 2021 |
| Description | Privitar |
| Organisation | Privitar |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | N/A |
| Collaborator Contribution | Privitar has committed to in-kind support of £170k over 5 years Activities could include collaborating on joint projects, advisory support and participation in engagement activity, dedicate or second specific staff, test and deploy porotype software tools, provide additional computational resources |
| Impact | N/A currently |
| Start Year | 2021 |
| Description | Quantexa |
| Organisation | Quantexa LTD |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | N/A |
| Collaborator Contribution | Estimated value of support over 5 years to be £1.18 million. In the form of advisory support and participation in engagement activity, computational resources. |
| Impact | N/A currently |
| Start Year | 2021 |
| Description | AI in the financial sector Podcast |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | With Dr Adrian Weller (Programme Director and Turing Fellow) and Kate Platonova (Group Chief Data Analytics Officer at HSBC), Ed Chalstrey discusses how AI is being used in financial services and what data is useful in banking today. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://turing.podbean.com/e/ai-in-the-financial-sector/ |
| Description | Blog piece on Homomorphic Encryption |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | Homomorphic encryption blog piece about the the future of secure data sharing in finance to help financial institutions tackle money laundering. The blog piece highlighted the collaboration between The Alan Turing Institute. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.turing.ac.uk/blog/homomorphic-encryption-future-secure-data-sharing-finance |
| Description | CWI Scientific Meeting 2022- Talk: Safety and robustness for deep learning with provable guarantees |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Topic: Safety and robustness for deep learning with provable guarantees- Computing systems are becoming ever more complex, with decisions increasingly often based on deep learning components. This lecture described progress with developing automated verification techniques for deep neural networks to ensure safety and robustness of their decisions. The lecture concluded with an overview of the challenges in this field. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.cwi.nl/en/events/special-scientific-meeting/ |
| Description | Data Sharing Workshop |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Workshop delivered by The Alan Turing Institute, in collaboration with HSBC for Practicable Data Sharing Across Borders project. The workshop provided an an overview of the different techniques and what differential privacy can offer to HSBC. Also provided an opportunity to identify use-cases to work on initially and HSBC presented ideas and challenge areas from within Global Banking and Compliance. |
| Year(s) Of Engagement Activity | 2022 |
| Description | Demonstration Differential Privacy and Combining Privacy-Enhancing Technologies (PETs) |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Demonstration to HSBC's compliance team took place on the proposed solution and implementation for research project Practicable Data Sharing Across Borders. This was well received and was attended by over 400 people from HSBC's compliance team and HSBC Lab, etc. The demonstration included 1) an introduction to privacy techniques 2) what formal privacy guarantee Data Privacy provides and 3) Data Privacy in combination with the other privacy-enhancing-technologies (PETs), and 4) Data Privacy in Finance. |
| Year(s) Of Engagement Activity | 2022 |
| Description | EPSRC-NSF Workshop on AI Strategy |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Policymakers/politicians |
| Results and Impact | Building upon this critical foundation, today the White House Office of Science and Technology Policy (OSTP) established the National Artificial Intelligence Initiative Office, further accelerating our efforts to ensure America's leadership in this critical field for years to come. The Office is charged with overseeing and implementing the United States national AI strategy and will serve as the central hub for Federal coordination and collaboration in AI research and policymaking across the government, as well as with private sector, academia, and other stakeholders. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://trumpwhitehouse.archives.gov/briefings-statements/white-house-launches-national-artificial-i... |
| Description | FAIR Theory of Change Workshop |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Other audiences |
| Results and Impact | Introduction to and about the FAIR Theory of Change framework, including stakeholder mapping and creating the FAIR Theory of Change. |
| Year(s) Of Engagement Activity | 2022 |
| Description | FAIR showcase |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Industry/Business |
| Results and Impact | FAIR showcase event at OXO Tower on 24 January 2023. The event was a success with high attendance and engagement (76 attendees,). The event invited leaders from academia and industry to join discussions about the responsible adoption of AI in the financial services industry, and included keynote speaker, Chief Data, Information & Intelligence Officer at the FCA, Jessica Rusu. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://www.turing.ac.uk/events/fair-showcase |
| Description | Federated ID Workshop Series |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Series of workshops on Federated ID |
| Year(s) Of Engagement Activity | 2022 |
| Description | Fintech and the Future of Money |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Speaker and host on event discussing future of finance. Contribution on security and privacy in fintech |
| Year(s) Of Engagement Activity | 2022 |
| Description | Future of Finance Roundtable |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | Roundtable on Future of Finance |
| Year(s) Of Engagement Activity | 2022 |
| Description | Interview with Mondato |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Professor Lukasz Szpruch was interviewed by the consulting firm Mondato for their blog on Synthetic Data and Fintech. The post was shared on LinkedIn. |
| Year(s) Of Engagement Activity | 2023 |
| URL | https://blog.mondato.com/synthetic-data-will-transform-fintech-ai-as-we-know-it/ |
| Description | Panellist, Cboe RMC, Reykjavik, October 2022 |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Panellist, Cboe RMC, Reykjavik, October 2022, industry audience, international |
| Year(s) Of Engagement Activity | 2022 |
| Description | Programme director for the SIAM Financial Mathematics and Engineering Activity Group |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Programme director for the SIAM Financial Mathematics and Engineering Activity Group |
| Year(s) Of Engagement Activity | 2023 |
| Description | Seminar at Boston University, about "Neural-SDEs and Market Models of Options" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Seminar, Boston University, February 2023, "Neural-SDEs and Market Models of Options" |
| Year(s) Of Engagement Activity | 2023 |
| Description | Seminar at Columbia University about "Neural Q-learning solutions to elliptic PDEs" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Mathematical Finance Seminar, Columbia University, 2023, "Neural Q-learning solutions to elliptic PDEs" |
| Year(s) Of Engagement Activity | 2023 |
| Description | Seminar at ETH Z\"urich about "Neural-SDEs and Market Models of Options" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Seminar, ETH Z\"urich, March 2022, "Neural-SDEs and Market Models of Options", academic audience, international |
| Year(s) Of Engagement Activity | 2022 |
| Description | Seminar at IMA conference on the mathematics of Big Data About "Neural Q-learning solutions to elliptic PDEs" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | IMA conference on the mathematics of Big Data, September 2022, "Neural Q-learning solutions to elliptic PDEs", academic audience, national |
| Year(s) Of Engagement Activity | 2022 |
| Description | Seminar at IMSI conference on acceptability indices about,"Asymptotic Randomised Control with applications to bandits" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | IMSI conference on acceptability indices, Chicago, May 2022,"Asymptotic Randomised Control with applications to bandits", academic audience, international |
| Year(s) Of Engagement Activity | 2022 |
| Description | Seminar at Illinois Institute of Technology (online) about "Gradient-based estimation of linear Hawkes processes with general kernels" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Seminar, Illinois Institute of Technology (online), March 2022, "Gradient-based estimation of linear Hawkes processes with general kernels", academic audience, international |
| Year(s) Of Engagement Activity | 2022 |
| Description | Seminar at North British Probability Edinburgh, "Neural-SDEs and Market Models of Options" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | North British Probability Seminar, Edinburgh, October 2022, "Neural-SDEs and Market Models of Options" |
| Year(s) Of Engagement Activity | 2022 |
| Description | Seminar at Oxford--Princeton mathematical finance meeting about "Asymptotic Randomised Control with applications to bandits" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Oxford--Princeton mathematical finance meeting, October 2022, "Asymptotic Randomised Control with applications to bandits", academic audience, regional/international |
| Year(s) Of Engagement Activity | 2022 |
| Description | Seminar at Princeton University, "Stability and approximation of projection filters" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Seminar, Princeton University, January 2023, "Stability and approximation of projection filters" |
| Year(s) Of Engagement Activity | 2023 |
| Description | Seminar at SIAM annual meeting about "Neural-SDEs and Market Models of Options" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | SIAM annual meeting, Pittsburgh, July 2022, "Neural-SDEs and Market Models of Options", academic audience, international |
| Year(s) Of Engagement Activity | 2022 |
| Description | Seminar at Stevens Institute of Technology Seminar about, "Neural-SDEs and Market Models of Options" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | Seminar, Stevens Institute of Technology Seminar, January 2023, "Neural-SDEs and Market Models of Options" |
| Year(s) Of Engagement Activity | 2023 |
| Description | The Impact of Emerging Technology RoundTable |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Third sector organisations |
| Results and Impact | RoundTable discussion with the Financial Conduct Authority (FCA) and University partners for project The Impact of Emerging Technology on the Financial Services Industry. Collaborative discussion on the landscape, scope and report structure around the impact of developments in AI on the financial services ecosystem, Web 3 and Quantum technologies. The RoundTable fostered further relationship development with the FCA. |
| Year(s) Of Engagement Activity | 2023 |
| Description | The Role of Synthetic Data in Financial Systems Workshop |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Industry/Business |
| Results and Impact | Workshop delivered by The Alan Turing Institute, in collaboration with HSBC for The role of synthetic data in financial systems. The workshop provided an overview of synthetic data, highlighted HSBC's identified use-cases in Time-Series, Fraud and Relational Database and demonstrated research capabilities for the project. |
| Year(s) Of Engagement Activity | 2022 |
| Description | World Online Seminar on Machine Learning in Finance about "Neural Q-learning solutions to elliptic PDEs" |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Postgraduate students |
| Results and Impact | World Online Seminar on Machine Learning in Finance, April 2022 "Neural Q-learning solutions to elliptic PDEs", academic audience, international |
| Year(s) Of Engagement Activity | 2022 |