AI and Data-driven innovation in Finance
The development and use of artificial intelligence (AI) is a challenging process in the financial sector.
Key issues include:
- Access to real financial data to train the AI – although data can be bought, it needs to be accurate as flawed data will produce a useless product.
- Dependence on large financial institutions for data – data is trapped in large institutions, so it is difficult for entities outside of these institutions to innovate at scale.
- Partnerships between large financial institutions and financial services innovators – often difficult in this highly regulated industry because of cultural and technical language differences. Mapping data so that it is in an agreed and usable format to be shared between entities can be costly and difficult.
We are working with several fintech firms, including PensionBee, Bud, Tink, Salary Finance and RISE, and with the Oxford Creative Destruction Lab, to identify:
- Successful entrepreneurial business models that use AI in finance and their key characteristics.
- The unique challenges that AI poses to entrepreneurial firms.
The findings of this research will inform policymakers at a global scale on:
- What challenges entrepreneurs face in the growth of such innovation.
- How to establish data and regulation sandboxes for entrepreneurial firms effectively.
- How varying customer data regulations have implications for AI-based innovation.