Quantum Machine Learning for Financial Fraud Detection: Generative Modelling and Adversarial Robustness on NISQ Quantum Processors

About the Project

Project Details

Financial crime, encompassing payment fraud, money laundering, identity theft, and insider trading, inflicts systemic damage on global economies. In the UK alone, fraud cost the banking industry $1.6 billion in 2024, and in April 2025 the UK government committed $161 million in quantum technology investment specifically to tackle crime, fraud and money laundering (World Economic Forum). Classical machine learning methods, whilst widely deployed, face structurally intractable challenges in this domain. Fraud datasets exhibit extreme class imbalance, with fraudulent transactions typically comprising less than 1% of records, non-stationary distributions, and high-dimensional feature spaces. These limitations reflect fundamental computational and statistical constraints that motivate exploration of different approaches.

Quantum Machine Learning (QML) offers a theoretically grounded alternative. Variational Quantum Circuits (VQCs) and quantum kernel methods embed classical data into exponentially large Hilbert spaces via parameterised quantum feature maps, enabling classifiers that operate in regions of function space inaccessible to polynomial-time classical algorithms. Quantum Generative Adversarial Networks (QGANs), and related quantum generative models such as Quantum Circuit Born Machines (QCBMs), utilise quantum superposition and entanglement to model complex, high-dimensional probability distributions, offering a principled mechanism for generating high-fidelity synthetic minority-class samples, directly addressing the class imbalance problem at its statistical root.

This PhD addresses three interconnected research challenges. First, it is not established which quantum feature maps and ansatz architectures are best suited to the distributional properties of financial crime data. Second, the question of whether QGAN-generated synthetic fraud data meaningfully improves quantum classifier performance, compared to standard quantum classifiers trained on imbalanced data alone, has not been rigorously answered on real hardware. Third, the adversarial threat surface of quantum financial crime detection systems remains uncharacterised: it is unknown whether VQCs and quantum kernel classifiers are inherently more or less robust than classical equivalents to adversarial perturbations crafted specifically in the financial crime domain.

The programme of work intends to proceed in three phases: (a) the design and execution of hardware-aware VQC and quantum kernel classifiers on IBM Quantum hardware, systematically investigating the role of data encoding strategies (amplitude, angle, and IQP-style encoding) and entanglement topology on classification performance for financial fraud data, (b) QGAN architectures will be trained and executed on quantum hardware, targeting the generation of statistically faithful synthetic fraudulent transaction data, and (c) a formal adversarial threat model will be developed for the quantum detection pipeline, covering input-space perturbation attacks, parameter-shift gradient leakage, and model extraction, with quantum-noise-aware defences evaluated on hardware.

The expected contributions include: (i) a hardware-validated, domain-specific framework for quantum feature map and ansatz co-design for financial crime data, (ii) the first systematic evaluation of QGAN/QCBM-augmented versus standard quantum classifiers for fraud detection on real quantum processors, (iii) a formal adversarial threat taxonomy and mitigation framework specific to quantum financial crime detection systems, and (iv) a cross-platform benchmarking spanning superconducting and trapped-ion architectures that provide empirically grounded guidance on hardware selection for financial QML applications.

This interdisciplinary project sits at the intersection of quantum computing, machine learning, cybersecurity, and financial technology, and offers the opportunity to contribute to an emerging field with significant scientific and societal impact.

Person Specification

Candidates should have been awarded, or expect to achieve, EITHER:

a] a First or Upper Second Class award in their undergraduate degree, in a relevant subject.

OR

b] a First or Upper Second Class award in their undergraduate degree, and a Merit or Distinction in a Masters degree, both in a relevant subject.

Qualifications from overseas institutions will be considered, but performance must be equivalent to that described above, and the University reserves the right to ascertain this equivalence according to its own criteria.

Essential:

• Solid foundation in mathematics, particularly linear algebra, probability, and optimisation.
• Proficiency in Python programming.
• Good understanding of machine learning principles (e.g., supervised learning, model evaluation).
• Strong analytical and problem-solving skills, with the ability to work independently and conduct research.

Desirable:

• Familiarity with quantum computing frameworks/SDKs (e.g., Qiskit, PennyLane).
• Experience with deep learning libraries (e.g., PyTorch, TensorFlow).
• Understanding of cybersecurity concepts or adversarial machine learning.
• Interest in financial systems, fraud detection, or FinTech applications.

Submitting an application

We can only consider applications that are complete and have all supporting documents. Applications that do not provide all the relevant documents will be automatically rejected.Your application must include:

1. English language copies of the transcripts and certificates for all your higher education degrees, including any Bachelor degrees.
2. A Research Statement detailing your understanding of the research area, how you would approach the project, and a brief review of relevant literature. Be sure to use the title of the research project you are applying for. There is no set format or word count.
3. A personal statement which outlines any further information which you think is relevant to your application, such as your personal suitability for research, career aspirations, possible future research interests, and further description of relevant employment experience.
4. A Curriculum Vitae (Resume) which details your education and work history.
5. Two academic refereeswho can discuss your suitability for independent research. References must be on headed paper, signed and dated no more than 2 years old. At least one reference should be from your most recent University. You can submit your references at a later date if necessary.
6. Evidence that you meet the English Language requirements. If you do not currently meet the language requirements, you can submit this at a later stage.
7. A copy of your passport. Where relevant, include evidence of settled or pre-settled status.

Contact Information

For enquiries about this project, contact Muhammad Shahbaz Khan (m.khan71@aston.ac.uk) and Jose Maria Alcaraz Calero (j.alcarazcalero@aston.ac.uk).

Location

This position will be based on the Aston Campus in Birmingham, UK. The successful candidate will need to be located within a reasonable distance of the campus, and will be expected to visit in person regularly.

Interviews

Interviews will be conducted online via Microsoft Teams. If you are shortlisted, you will be contacted directly with details of the interview.

Apply for this position here

Funding Notes

This fully funded PhD covers 100% of tuition fees and provides an annual tax-free stipend at the UKRI rate (£21,805 for 2026/27), with increases in line with UKRI guidance for the duration of the studentship.

Please note that the successful candidate will be responsible for any costs relating to moving to Birmingham and/or visiting the Aston campus. International students must meet the financial requirements for the visa, flights, and NHS Surcharge. Applicants should be confident that they can meet these costs before applying.

Further information can be found here: Financial Requirements | Aston University