Trustworthy AI in financial technology, customer experience, and operational performance

How can financial technology AI innovations, enhance customer experience and trust while improving operational performance

Overview

The unstoppable algorithmic transformation of financial services has seen the financial technology (FinTech) industry surge from the sidelines to the mainstream. FinTech is broadly defined as a permanent technology revolution that is changing the way we do finance. While narrow artificial intelligence (AI) and machine learning (ML), which uses rule-based algorithms, has dominated the fast-paced automation of tasks and finance, the next wave of automation will be digitising judgment calls. Given that finance professionals have an essential fiduciary duty towards their clients, the rapid growth of artificial intelligence (AI) in finance has highlighted some critical risks around trust, overfitting, lack of interpretability, biased inputs and unethical use of data.

This funded PhD offers a unique opportunity to understand how digital innovations affect a highly valued financial service provision experience. Using access to member-level big data this project seeks to understand the risk and opportunities AI and ML innovation technologies present to the financial service delivery and operational effectiveness. Specifically, the research project addresses three broad areas:

  1. Regulatory technology and the effective management of financial institution stability and growth.
  2. ML innovations to default risk prediction and the development of new products.
  3. Customer experience innovations using AI and ML techniques and digital platforms.

The successful candidate will be supervised Dr Barry Quinn. Barry leads out on fintech and data science for the management school, and has been involved with industry collaborations valued at over £1.5M. Barry holds a professional chartered status from Royal Statistical Society professionally chartered for his work in data science and applied statistics. He has extensive experience in PhD supervision. Most notably, he supervises industry relevant projects in interpretable machine learning and econometrics, financial machine learning, and financial regulation and risk analytics. He also has two new PhD students who are exploring regulatory technology and financial inclusion, ESG fair value analytics.

Collaborative Industry placement

The goal of this project is to achieve output with significant impact for the industry partner. The successful candidate will liaise closely with key industry stakeholders and have direct industry experience via a work placement.

Funding and candidate criteria

The position is fully funded by a CAST studentship fro the Northern Ireland Department of the Economy (DfE).

The value of a DfE studentship for UK domiciled students the value of an award includes the cost of approved fees as well as maintenance support. In academic year 2021-2022 the basic rate of maintenance support for a Research Studentship is £15,285 (tax-free) while the basic rate of maintenance support for a Taught Studentship is £7,643. For non-domiciled UK candidate please read the eligibility criteria for DfE studentships here.

Apply online at: https://dap.qub.ac.uk/portal/user/u_login.php and enter the text QMS2022FIN/AIML into the funding field. The application deadline is September 30th 2022, with shortlisted candidates being asked to interview shortly after this date. The successful candidate would ideally be able to start immediately, but no later than 30th October 2021.

Candidate criteria

  1. 2.1 Honours degree or equivalent qualification (or equivalent) in Finance, Computer Science, Mathematics, or a related subject. A Master’s degree (or equivalent professional qualification acceptable to the University) will normally be required. Extensive professional experience may be considered in lieu of a Master’s degree on a case-by-case basis.
  2. Relevant work experience might include work in one of the following areas: a. advanced statistical analysis b. machine learning and AI c. predictive analytics d. natural language processing e. advanced data visualisation
  3. Proficiency in one or more program languages, such as R or Python or equivalent.
  4. The ability to work independently and collaboratively at the frontier of research knowledge with a growth mindset.
Barry Quinn
Barry Quinn
Senior Lecturer in Financial Technology and Data Science

Barry