A new UK research network called ACORN is bringing together universities, financial firms, regulators, and consumer groups to tackle the industry’s toughest problems—from ethical AI and green finance to a shrinking pool of skilled workers. These “wicked problems” are too complex for any single discipline to solve. ACORN’s mission is to bridge the gap between academic research and the real-world needs of financial services, its customers, and its regulators. The network, with regional hubs in Wales, Scotland, North-East England, and London, will connect expertise across the country. If successful, ACORN could reshape how the financial industry innovates. Instead of chasing technology for its own sake, firms would have access to research that accounts for ethics, environmental impact, and public trust. The network offers five types of collaborative projects, from quick seed-funding experiments to larger feasibility studies and impact projects that can influence policy or launch commercial products. Shared resources—a secure data vault, a code base, and template legal agreements—lower the barriers to collaboration. The goal is a more inclusive, responsible financial system that works for everyone, not just the largest players.
View original technical description
The ACORN network's mission is to bridge the gap that currently exists between the research in universities and the need of the financial services industry, its consumers and the regulator. ACORN wants to grow to well over 100 primary partners and 1000 associated partners, offering an inclusive, diverse and responsible research culture. Based on regional presence in Wales, Scotland, North-East England and London, it will harmonize technological know-how across regions and connect regional partners to nation-wide efforts. Real-life challenges in financial services are complex, combining responding to technology innovation with business ethics, green/environmental considerations and scarcity in the talent pipeline. This presents FS with wicked problems, which the industry cannot ignore, and which require people and researchers from across disciplines to come together. ACORN aims to address wicked problems in FS that are associated with innovation in technology, mathematics and sciences. ACORN provides a number of mechanisms to succeed in this mission. Central to ACORN's working is its 'commissioning framework', which provides the funding mechanisms for five types of collaborative projects between academia and partners. ACORN offers seed project funding, which aims to explore technological, mathematical and scientific solutions for real-life challenges in FS, prioritised through co-design sandpits. It then offers funding for larger multi-disciplinary feasibility projects, which may build on the seed projects, and expand to consider 'wicked' multi-disciplinary research problems. In parallel, ACORN offers funding for agile projects, which can be of any type, e.g., horizon scanning, population survey, a software prototype or a machine learning application. These have predetermined IP arrangements, so that they can be organised in agile manner and can start at any time for the duration of ACORN. Additionally, impact projects are offered to take any of the research projects further (e.g., to influence policy makers, or initiate commercialisation), and education/engagement projects allow to grow the FS talent pool and address the talent pipeline. To support researchers and partners in these project, ACORN establishes a number of services the community can use. The co-design service and the corporate digital responsibility service help researchers to consider these aspects in their proposals. The secure data vault, the shared code base, the experimentation sandbox and template IP arrangements are available to improve research, its impact and to lower collaboration barriers. We name the network ACORN, to signify that collaborations as majestic as an oak tree can grow from humble beginnings.
Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.
Is something wrong? Let us know