"PhD Studentship - Agentic AI in Retail Investing: Security, Resilience, and Trust"

PhD Studentship - Agentic AI in Retail Investing: Security, Resilience, and Trust

Manchester Metropolitan University

Placed On: 4th December 2025
Closes: 2nd March 2026
Reference: SciEng-MAK-2026-27-Retail AI Security

Project advert

The democratization of finance is transforming global asset management. Retail investors, empowered by trading apps and AI-driven platforms, now control a rapidly expanding share of global assets projected to reach 62% by 2030. While this growth fosters financial inclusion, it also creates new and underexplored risks at the intersection of technology, human behaviour, and market structure.

This PhD project will investigate systemic financial risks emerging from the convergence of agentic artificial intelligence and retail trading platforms. Using a combination of experimental analysis, threat modelling, and proof-of-concept demonstrations, the research will identify and map vulnerabilities that may impact both individual investors and global financial stability, as well as evaluate their broader socio-economic implications.

The successful candidate will join a multidisciplinary supervisory team of experts in AI, cybersecurity, and finance, based in the state-of-the-art £117M Dalton Building at Manchester Metropolitan University. You will develop skills in computational experimentation, financial data analytics, and systemic risk modelling, with extensive opportunities for publication, conference participation, and industry collaboration.

As part of our inclusive and supportive doctoral community, you will benefit from tailored training, mentorship, and professional development designed to prepare you for a career at the intersection of technology and finance.

Project aims and objectives

This project aims to investigate the financial and systemic risks arising from the integration of agentic artificial intelligence and retail trading platforms (neo-brokers), and to assess their broader socio-economic consequences for investors and markets.

The research will identify and characterise emerging vulnerabilities through experimental analysis, threat modelling, and proof-of-concept demonstrations, evaluate the potential impacts of these risks on retail investors and financial stability, and develop approaches to enhance security and resilience, helping to build trust in AI-enabled retail investment ecosystems.

Funding

Both Home and International students can apply. Only home tuition fees will be covered for the duration of the 3.5-year award, which is £5,006 for the year 2025/26. Eligible international students will need to make up the difference in tuition fee funding (Band 2 for the year 2025/26).

The student will receive a standard stipend payment for the duration of the award. These payments are set at a level determined by the UKRI, currently £20,780 for the academic year 2025/26.

Specific requirements of the candidate

The successful applicant is expected to have:

• An honours degree at first or upper second-class level in Cyber Security, Artificial Intelligence, Computer Science, or other related academic discipline.
• Good programming skills (preferably Python).
• Background/work experience in Cyber Security, Machine Learning, and Finance would be highly beneficial.

How to apply

Interested applicants should contact Dr Mohammed Al-Khalidi (M.Al-Khalidi@mmu.ac.uk) for an informal discussion.

To apply you will need to complete the online application form for a full time PhD in Computing & Digital Technology.

Please complete the Doctoral Project Applicant Form, and include your CV and a covering letter to demonstrate how your skills and experience map to the aims and objectives of the project, the area of research and why you see this area as being of importance and interest.

Please upload these documents in the supporting documents section of the University’s Admissions Portal.

Applications closing date: 2nd March 2026

Expected start date: October 2026

Please quote the reference: SciEng-MAK-2026-27-Retail AI Security