Research

The Centre’s research addresses challenges at the intersection of finance, technology and regulation. It is grounded in econometric inference, causal reasoning and financial theory, and critically evaluates emerging computational methods where evidence supports their use. Research is organised around four discipline-led themes, with two cross-cutting application tracks.

Theme 1 — Responsible models and governance

Governance of algorithmic systems in financial markets: how models used in lending, compliance and surveillance can be made transparent, auditable and accountable. Includes computational approaches to regulation and market surveillance, explainability and fairness assessment, and the validation and control of algorithmic decision systems.

This theme anchors the Centre’s two Collaborative Doctoral Partnership scholarships in financial-crime detection and anti-money-laundering investigator training, and the developing CFRT/AICC white paper on AI in financial services.

Theme 2 — Causal and econometric methods for risk and policy

Causal inference and econometric modelling for systemic risk, credit risk and policy evaluation, prioritising identification and uncertainty quantification over correlational pattern-finding. Active work includes Bayesian systemic-risk modelling in European banking and research on climate risk in credit unions.

Theme 3 — Market microstructure and trading analytics

Asset pricing, portfolio construction and trading-signal research, with explicit, like-for-like comparison of machine-learning methods against interpretable baselines. The emphasis is partial explainability with an honest account of what remains unexplained.

Theme 4 — Financial text and document intelligence

Natural-language and foundation-model methods applied to corporate disclosure, sentiment, patents and other financial documents, with human evaluative judgement embedded in the workflow. This theme anchors the patent-text doctoral scholarship.

Application tracks (in development)

• Climate finance: climate-risk modelling and the governance of measurement, reporting and verification infrastructure.
• AI ethics: explainability, fairness assessment and privacy in financial decision systems.

Research infrastructure

Through a developing collaboration with the University’s AI Collaboration Centre, the Centre accesses high-performance computing and specialist data-science expertise, supporting collaborative research without replicating that infrastructure independently.