Conor B. Hamill

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. We hope that this can serve as a quick guide for data scientists, professionals in the financial sector, and enthusiasts in this area to understand why quantum computing and QML in particular could be interesting to explore in their field of expertise.

Interest-free promotions are a prevalent strategy employed by credit card lenders to attract new customers, yet the research exploring their effects on both consumers and lenders remains relatively sparse. Selecting an optimal promotion strategy is intricate, involving the determination of an interest-free period duration and promotion-availability window, all within the context of market dynamics and complex consumer behaviour. The purpose of this study is to develop an agent-based model to assist with determining optimal promotion strategies. In this paper, we introduce a novel agent-based model that facilitates the exploration of various credit card promotions under diverse market scenarios. Our experiments reveal that, in the absence of competitor promotions, lender profit is maximised by an interest-free duration of approximately 12 months, while market share is maximised by offering the longest duration possible. In the context of concurrent interest-free promotions, we identify that the optimal lender strategy entails offering a more competitive interest-free period and a rapid response to competing promotional offers. Notably, a delay of three months in responding to a rival promotion corresponds to a 2.4% relative decline in income. Our model consists of multiple lender and consumer agents that interact through a novel set of mechanisms based on well-studied consumer behaviours. Distinct from previous works, our model adopts a realistic billing cycle with a focus on interest charged to revolving accounts and supports a range of lender promotion strategies. It is calibrated to historical benchmarks and validated against both stylised facts and time-series data, ensuring a realistic reflection of market behaviour, which has been neglected in prior studies.