海角黑料

You are here:  

• Print

Researchers at the Global Centre for Banking and Financial Innovation (GCBFI) secure funding from a variety of prestigious sources and funding bodies. Our projects span SME risk assessment, financial inclusion, and AI‑driven innovation, delivered in partnership with leading organisations to address key challenges in finance.

An overview of our current funded research projects, reflecting the breadth and impact of our work across the financial sector.

The project comprises three main strands: advanced training in Reinforcement Learning at the Massachusetts Institute of Technology (MIT); innovative research into tail risk management using RL techniques; and a cross-disciplinary workshop hosted by Nottingham University Business School. The workshop will introduce AI and RL methodologies to a broad academic audience, fostering knowledge exchange across disciplines.

Project outcomes:

The project enhanced capability within the Midlands research community to apply Artificial Intelligence (AI) and Reinforcement Learning (RL) methods in management research. It also strengthened cross-disciplinary collaboration between academic researchers across related fields.

---

 

Project overview:

Fund managers play a central role in making responsible investment decisions on behalf of their clients. This project explored how Artificial Intelligence (AI) and Machine Learning (ML) are being adopted within fund management, and whether these technologies can improve investment performance, operational efficiency and risk management.

The project explored why and how fund managers are using artificial intelligence and machine learning, the challenges they face when adopting these technologies, and how rules and regulations may affect their use, particularly in the UK. It also looked at wider patterns in how technology is being used across the asset management sector, alongside the potential benefits and risks involved.

Project outcomes:

The project provided stakeholders with clear insights into how fund management firms were using AI and ML technologies and what this could mean for future developments in applied financial technology. It contributed to advancing knowledge and innovation in the use of AI and ML in finance, while fostering meaningful collaboration between academic researchers and industry partners.

 

---

 

Project overview:

This project examined the complex relationships between financial inclusion, financial literacy, financial fragility and poverty in Brazil. It aimed to generate evidence based insights by accessing rich administrative and financial datasets, including PIX, the Central Bank Credit Dataset and Social Income datasets.

Project outcomes:

The project generated clear, research based insights into the benefits of financial inclusion and financial literacy initiatives in Brazil, helping to inform policymakers and industry stakeholders. It also established collaborative research channels between the 海角黑料, the Central Bank of Brazil, CAIXA, and Universidade Federal de Santa Catarina, supporting future joint research activities.

 

---

 

Project overview:

This project evaluated an innovative digital financial education programme targeted at underserved rural communities in the Rwenzori region of Uganda. It also informed farmers of climate change and possible mitigations they could consider.

 

---

 

Project overview:

This project applied artificial intelligence and big data to develop a Pandemic Risk Index and an AI‑based credit‑risk assessment tool that help policymakers and lenders better understand COVID‑19’s impact on UK SMEs and allocate financial support more effectively in collaboration with the Bank of England and the CBI.

 

---