Personal Statement
Swati is a Lecturer in Artificial Intelligence in Finance (Financial Technology). Her research focuses on the development of explainable AI methods for automating, augmenting, and auditing high-stakes decisions in finance, insurance, and law. She is particularly interested in the role of Explainable AI, Blockchain, and Post-Quantum Cryptography in building trustworthy, privacy-preserving, and accountable decision-support systems. (Link to Swati's GitHub Profile).

Her research impact case on financial inclusion and legal equality for underserved communities integrates Explainable AI, cryptographic security, and blockchain to develop trustworthy decision-support systems. This work is supported by FinTech firms, with computational support from Chain Crunch Labs. She publishes regularly in IEEE, IET Conferences, and AI journals.

Swati has an MSc in Operational Research from the School of Mathematics, University of Edinburgh, and a PhD in Operational Research and Applied Statistics from the School of Engineering, Glasgow.

After completing her PhD, she worked at the University of Nottingham as a Research Fellow on a European Commission project on Innovative Intelligent Rail (In2Rail). Later, she joined Alliance Manchester Business School, the University of Manchester, where she worked on an AI Smart Claim insurance project funded by UKRI in collaboration with Kennedys Law and AXA Insurance. She has been involved in FinTech automation projects with start-ups, financial institutions, and law firms to create explainable AI decision-support systems.