Financial Technology


Currently, financial services are embracing new and innovative technologies in areas such as asset management, banking, credit risk management, and specific broad-based aspects of financial services such as fraud detection, anti-money laundering (AML), or know-your-customer (KYC). For example, regulatory technology (RegTech), defined as the utilisation of information technology for regulatory purposes, may offer the potential to improve and automate many processes undertaken by the supervisory bodies of the financial industry e.g. Central Banks, as well as providing better risk identification within financial institutions (FIs).

Within much of this activity, two technologies are seen to be key. The first is artificial intelligence (AI) and the second is blockchain technologies.

Financial services in the UK sector is a major contributor to the economic output of the country and a major source of employment (2/3 of jobs are outside of London). It is, therefore, imperative to understand the forces and risks behind potentially disruptive technologies used in the financial sector as well as the competitive advantages these technologies may bring to this sector.

The common theme of this project, to contrast the benefits (efficiency gains and better risk identification) and risks (impact on jobs and cyber threats) that come with the application of technology in financial services and to develop governance frameworks that can be used by both financial services providers and regulators to evaluate the costs and benefits of these new technologies.

Both the academic and policy debates around deficiencies in the regulatory process conclude that technological innovations such as machine learning may help regulators decide on regulatory actions. For example, the FCA has introduced its regulatory sandbox where the main objective is to test the regulatory compliance of FIs and products in a controlled, simulated environment. Despite the rapid progress in terms of applications in various areas of financial services and regulation, the reliability of these techniques is yet to be assessed with any real rigour. The production of such evidence will allow regulators to be more transparent about the choice of techniques used in the regulatory process and will enable firms to better deploy such technologies in the future.

There is significant value in cross-discipline research in these areas. From a social science perspective, a detailed examination of issues of productivity, jobs, growth, etc. can only be understood in the context of a deep understanding of the technology. Whereas from a computer science and technological perspective, this is tangible work in a domain where the applications are both challenging and innovative. To build expertise across these areas creates not only original thinking but innovative methodologies and approaches to answering fundamental questions about the economy today and the shape of the economy tomorrow.