Profile
I am a PhD candidate in Swansea University’s Centre for Doctoral Training (CDT) program, working in partnership with HSBC and funded by the Engineering and Physical Sciences Research Council (EPSRC). My research investigates multi-channel customer journey modelling in the financial services sector, aiming to better understand customer behaviour across digital and physical commercial banking channels. My work integrates Natural Language Processing (NLP), topic modelling, visualisation, and behavioural analytics to uncover patterns in unstructured customer feedback data.
Building on my MSc, also completed at Swansea as part of a joint programme, I specialise in embedding techniques, explainability in AI systems, and customer experience analytics. I am particularly interested in how machine learning models can support ethical and interpretable decision-making in finance.
Area of Expertise
- Multi-Channel Behavioural Analytics
- Application of Natural Language Processing and Large Language Models
- Topic Modelling and Customer Insight Extraction
- Explainable Artificial Intelligence in Regulated Environments
- Secure Infrastructure Deployment for Machine Learning
Projects
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Modelling and Optimising Multi-Modal Customer Journeys in Commercial Banking: A Human-Centred Approach to Behavioural Analytics and Outcome Prediction Across Digital, Physical, and Telephony Channels
Project Overview This research investigates the use of Human-Centred AI to model and optimise customer journeys in UK commercial banking. The project focuses on multi-modal customer interactions across digital, physical, and telephony channels, such as mobile apps, online platforms, branch visits, and call centre experiences. Using topic modelling techniques on unstructured customer feedback (including surveys,…