Generative AI for AML Investigator Training: Synthetic Scenario Development

Summary

AML investigators must recognise diverse money laundering typologies, but training is constrained by privacy regulations and rare-event scarcity.

Traditional case-based training relies on limited historical examples, leaving investigators poorly prepared for emerging threats.

This PhD will develop and validate generative AI models to create realistic synthetic suspicious activity scenarios for investigator training.

You will fine-tune GPT-based models on historical suspicious activity reports using privacy-preserving techniques (differential privacy, k-anonymity) to generate diverse scenarios spanning money laundering typologies such as smurfing, trade-based laundering, and shell company structures.

You will rigorously evaluate whether synthetic scenarios achieve pedagogical effectiveness and regulatory acceptance through controlled training studies with real AML investigators.

Working with Napier AI, a financial crime prevention software provider, you will complete placements (minimum three months cumulative) at their Belfast office, gaining exposure to operational AML contexts and access to compliance professionals for validation studies.

Training spans advanced NLP (transformer fine-tuning), financial crime typologies, privacy-preserving machine learning, and product-oriented development.

You will learn to design rigorous evaluation studies measuring detection accuracy, false positive rates, and investigator trust in AI-generated materials.

Outputs include a proof-of-concept scenario generator (Python toolkit), evaluation metrics suite, and academic papers in top finance and information systems venues.

We welcome applicants with backgrounds in computer science, data science, or computational linguistics.

Skills required of the applicant:

Essential:

• Strong programming skills in Python (NumPy, pandas, scikit-learn, PyTorch or TensorFlow)
• Natural language processing fundamentals and text analysis
• Statistical modeling and experimental design for rigorous evaluation
• Clear technical writing for documentation and academic publication

Desirable:

• Experience with transformer models (GPT, BERT, LLaMA) or large language model fine-tuning
• Privacy-preserving machine learning techniques (differential privacy, federated learning)
• Financial compliance, regulatory technology, or financial crime prevention domain knowledge
• Experience conducting user studies or human-subject experiments

Personal attributes:

• Strong interest in responsible AI and ethical machine learning
• Comfortable with interdisciplinary work bridging computer science and finance
• Ability to engage effectively with industry stakeholders and practitioners
• Intellectual humility to acknowledge limitations and value negative results

Essential criteria

Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.

We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.

In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.

• A comprehensive and articulate personal statement
• Research proposal of 2000 words detailing aims, objectives, milestones and methodology of the project

Desirable Criteria

If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.

• First Class Honours (1st) Degree
• Masters at 70%

Equal Opportunities

The University is an equal opportunities employer and welcomes applicants from all sections of the community, particularly from those with disabilities.

Appointment will be made on merit.

Funding and eligibility

This project is funded by:

• Department for the Economy (DfE)

Our fully funded PhD scholarships will cover tuition fees and provide a maintenance allowance of £21,000 (approximately) per annum for three years* (subject to satisfactory academic performance).  A Research Training Support Grant (RTSG) of £900 per annum is also available.

These scholarships, funded via the Department for the Economy (DfE), are open to applicants worldwide, regardless of residency or domicile.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

*Part time PhD scholarships may be available to home candidates, based on 0.5 of the full time rate, and will require a six year registration period.

Due consideration should be given to financing your studies.