Future Finance 4 All, led by the University of Bristol, will take a mission led approach to accelerate innovation adoption in Mid-Tier organisations and small and medium-sized enterprises (SME), in the UK Financial Services (FS) Sector across the four UK home nations. The focus of this partnership is to enhance the sector's productivity and global competitiveness. To achieve this, we will develop an understanding, from a social science perspective, of the drivers and obstacles to innovation uptake in this target group. We will then put in place a mission-oriented approach that leverages both leading social science research and experience in supporting SME innovation adoption to inform the development of an innovation adoption accelerator. The accelerator will be delivered over three phases, Phase 1-Local, Phase 2-Regional, Phase 3-National. Working with partners, including policy makers, industry and community organisations, the accelerator will help us overcome obstacles and drive innovation adoption across UK regional FS clusters. This will overcome the market failures that are holding back innovation uptake, unlocking productivity and levelling benefits across the UK regions. The accelerator will also enable us to also tackle societal challenges around responsible access and uptake of FS for underserved communities, individuals and companies. This will lead to the development of new bespoke products and services, which the Mid-Tier organisations and SMEs, which are the accelerator's focus, could then exploit. This potential 'market making opportunity' for new FS product and service innovation could have relevance in both UK and global markets that share similar inclusion challenges. The innovation accelerator activities will facilitate networking and partnerships between social science experts and the financial services community through innovator pathway fellows', drawn from high potential early career researchers. Building on our partnership's research base and expertise supporting innovation clusters, we will then deliver a rolling collaborative challenge programme that brings together industry, academic and social insights to explore and address barriers to innovation adoption. Through a rolling programme and digital platform the challenge programme outputs will inform the development of specific interventions for FS firms and stakeholders to enable them to gain the skills and capabilities to innovate. To maximise engagement and efficiency the innovation skill & training Programme will be delivered in scalable hybrid format and include peer-to-peer learning. Foundational to the Programme will be a focus on inclusive growth and diversifying the talent pipeline, addressing key findings from the 2022 UoB-led FinTech report, Kalifa Review, cross-sector surveys (EY and Innovate Finance, 2022), and sector-wide consultations. The accelerator will support the creation of habit-forming behaviour change through the exploitation of the Quadruple Helix model that brings universities, underserved communities, industry (including the sector's charities and not for profit players) and government to: Better connect key actors across the FS sector to overcome fragmentations, this will build new skills and capabilities within the partners and the project team. Ensure that the voices of underserved communities, individuals and companies are heard and reflected in the tangible delivery of new, or enhanced, FS products and services. Stimulate and support industry to prioritise innovation investment. Provide pathways, and practical solutions, to enable innovation uptake, including digital innovation, that enhances the productivity of mid-tier FS organisations and SMEs. A key project output will the measurement of these productive gains and their impact on the organisations that we support, and how this will contribute towards UK regional levelling up by unlocking a broad spectrum of organisational, economic and social benefits

AI and Machine Learning often address challenges that are relatively monolithic in nature: determine the safest route for an autonomous car; translate a document from English to French; analyse a medical image to detect a cancer; answer questions about a difficult topic. These kinds of challenge are very important and worthwhile targets for AI research. However, an alternative set of challenges exist that are more *collective* in nature and that unfold in *real time*: - help minimise the impact of a pandemic sweeping through a population of people by informing the coordination of local and national testing, social distancing and vaccination interventions; - predict and then monitor the extent and severity of an extreme weather event using multiple real-time physical and social data streams; - anticipate and prevent a stock market crash caused by the interactions between many automated trading agents each following its own trading algorithm; - derive city-wide patterns of changing mobility from high-frequency time series data and use these patterns to drive city planning decisions that maximise liveability and sustainability in the future city; - assist populations of people with type 2 diabetes to avoid acute episodes and hospitalisation by identifying patterns in their pooled disease trajectories while preserving their privacy and anonymity. Developing AI systems for these types of problem presents unique challenges: extracting reliable and informative patterns from multiple overlapping and interacting data streams; identifying and controlling for inherent biases within the data; determining the local interventions that can allow smart agents to influence collective systems in a positive way; developing privacy preserving machine learning and advancing ethical best practices for collective AI; embedding novel machine learning and AI in portals, devices and tools that can be used transparently and successfully by different types of user. The AI for Collective Intelligence (AI4CI) Hub will address these challenges for AI in the context of critically important real world use cases (cities, pandemics, health care, environment and finance) working with key stakeholder partners from each sector. In addition to significantly advancing applied AI research for collective intelligence, the AI4CI Hub will also work to build *community* in this research area, linking together academic research groups across the UK with each other and with key industry, government and public sector organisations, and to build *capability* by developing and releasing open access training materials, tools, demonstrator systems and best practice guidance, and by supporting the career development of early and mid-career researchers both within academia and beyond. The AI for Collective Intelligence Hub will be a centre of gravity for a nation-wide research effort applying new AI to collective systems.

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.