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2025 publications

AUTOSUMM: A Comprehensive Framework for LLM-Based Conversation Summarization

We present AUTOSUMM, a large language model (LLM)-based summarization system to generate accurate, privacy-compliant summaries of customer-advisor conversations. The system addresses challenges unique to this domain, including speaker attribution errors, hallucination risks, and short or low-information transcripts. Our architecture integrates dynamic transcript segmentation, thematic coverage tracking, and a domain specific multi-layered hallucination detection module that combines syntactic, semantic, and entailment-based checks.

Accepted to the Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Industry Track)

How Personality Traits Shape LLM Risk-Taking Behaviour

This research investigates the relationship between personality traits and risk-taking behaviour in Large Language Models (LLMs) using Cumulative Prospect Theory and the Big Five personality framework. The study reveals that most examined LLMs function as risk-neutral rational agents while exhibiting higher Conscientiousness and Agreeableness with lower Neuroticism. Interventions targeting Big Five traits, especially Openness, successfully influence risk-propensity in several models. Advanced LLMs demonstrate human-like personality-risk patterns through optimal prompting, while their distilled variants show limitations in cognitive bias transfer. The research identifies Openness as the most significant factor affecting risk-propensity, consistent with human baselines, and highlights both potential and limitations of personality-based interventions in LLM decision-making.

Accepted to the Findings of the Association for Computational Linguistics: ACL 2025

Application of GraphSAGE in Complex Transaction Networks

We present the practical application of GraphSAGE, an inductive Graph Neural Network framework, to non-bipartite heterogeneous transaction networks within a banking context. We construct a transaction network from anonymised customer and merchant transactions and train a GraphSAGE model to generate node embeddings. Our exploratory work on the embeddings reveals interpretable clusters aligned with geographic and demographic attributes. Additionally, we illustrate their utility in downstream classification tasks by applying them to a money mule detection model where using these embeddings improve the prioritisation of high-risk accounts. Beyond fraud detection, our work highlights GraphSAGE’s adaptability to banking-scale networks, emphasising its inductive capability, scalability, and interpretability. This study provides a blueprint for financial institutions to harness graph machine learning for actionable insights in transactional ecosystems.

Accepted to the Proceedings of The Workshop on Graph-based Representations in Pattern Recognition 2025 (GbR 2025)

2024 publications

A Brief Review of Quantum Machine Learning for Financial Services

This review paper examines state-of-the-art algorithms and techniques in quantum machine learning with potential applications in finance. We discuss QML techniques in supervised learning tasks, such as Quantum Variational Classifiers, Quantum Kernel Estimation, and Quantum Neural Networks (QNNs), along with quantum generative AI techniques like Quantum Transformers and Quantum Graph Neural Networks (QGNNs). The financial applications considered include risk management, credit scoring, fraud detection, and stock price prediction. We also provide an overview of the challenges, potential, and limitations of QML, both in these specific areas and more broadly across the field.

 

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2023 publications

Agent-based Modelling of Credit Card Promotions

In this research work, we develop an agent-based model of the UK credit card market, based on the interactions between lenders and customers. We then show how this model can be used as a tool to explore outcomes of zero-interest credit card promotion strategies under different market scenarios.

 

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Conformal Predictions for Longitudinal Data

In this paper, we present our research into uncertainty calculation for multi-dimensional time-series forecasting, setting a new performance benchmark for the field. These uncertainty estimates allow more informative predictions in areas like demand and stock market prediction and boost the trustworthiness of our forecasts.

 

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Modelling customer lifetime-value in the retail banking industry

This research improves the accuracy of customer lifetime value estimation in retail banking through a machine-learning framework. The improved insight from this framework allows accurate identification of high-value customers, informing marketing, relationship management, and business growth strategies.

 

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2022 publications

Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit

In this paper, we train an offline reinforcement learning agent with a static dataset to determine a better loan interest pricing policy. This approach can be applied to other pricing tasks and means risky experimentation in a live environment can be avoided.

 

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An Introduction to Machine Unlearning

This paper presents a comprehensive review of a wide range of machine unlearning algorithms, which remove the influence of individual observations from models while minimising computational costs. This review standardizes definitions, evaluation methods, and tackles implementation challenges for machine unlearning.

 

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