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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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