The programme aims to equip students with foundational knowledge of behavioural finance, advanced technical analysis, and practical algorithmic programming skills tailored to quantitative investing. Following a logical progression from market structure, price action, and indicator taxonomy to platform scripting (Pine Script and MQL5), systematic backtesting, generative artificial intelligence (GenAI) strategy integration, and institutional risk modelling, students will learn to design, code, and evaluate automated trading systems that exploit market inefficiencies and optimise portfolio execution. The programme also cultivates students’ ability to apply quantitative tools and programmatic indicators to solve real-world investment problems, while embedding systematic risk management, bias mitigation, and governance of emerging technologies to deliver robust, risk-adjusted trading strategies.

The programme aims to equip students with foundational knowledge of generative artificial intelligence (GenAI) and practical Python skills tailored to finance and business. Following a logical progression from core syntax and large language model application programming interfaces (LLM APIs) to data automation, financial analytics, agentic workflows, and compliant deployment, students will learn to design, build, and deploy intelligent systems that enhance decision-making, automate complex operations, and drive innovation. The programme also cultivates students’ ability to apply GenAI tools and Python code to solve real-world financial problems while embedding ethical, governance, and regulatory awareness to deliver responsible, production-ready solutions.

This programme aims to provide students with the strategic and technical foundations required to integrate embodied artificial intelligence (EAI) and spatial finance into modern financial architecture. It discusses the economic implications of Artificial General Intelligence (AGI), the technical components of EAI, such as robotics and sensor technologies, and their application in the financial sector. Students will learn to utilise geospatial data for advanced financial analytics and to explore digital twin ecosystems for asset management and innovative financial applications. The programme also equips students with a no-code platform featuring generative AI to explore practical applications of spatial finance.

The programme aims to equip students with the essential capability to integrate artificial intelligence (AI) with modern investment analysis, and to systematically master the core principles and practical implementation techniques of large language models (LLMs) and retrieval-augmented generation (RAG) systems. Students will learn to design and build efficient, reliable, and intelligent investment analysis systems capable of extracting key insights from financial data. Students will also develop the skills to solve real-world financial problems, addressing key challenges such as temporal data processing, multimodal analysis, and the design of methods to mitigate hallucinations. Besides, the programme cultivates students’ ability to apply computational tools and software for generative artificial intelligence (GenAI) and LLMs to perform AI-driven investment analysis.

The programme aims to provide students with strategic and technical perspectives in the transition from traditional digital banking to artificial intelligence-driven autonomous finance. Students will explore the convergence of Web 4.0 autonomous agents, programmable money, and tokenised real-world assets (RWAs) to understand how machine-to-machine commerce is reshaping global markets. By examining multi-agent systems, privacy-preserving technologies like zero-knowledge proofs, and regulatory-as-code frameworks, the programme covers the architecture and management of invisible financial ecosystems. The programme also equips students with the multidisciplinary expertise required to design, govern, and scale autonomous financial systems that are secure, compliant, and hyper-personalised within a rapidly evolving, modular, decentralised finance (DeFi) landscape.

The programme aims to provide students with contemporary knowledge of generative artificial intelligence (GenAI) and large language models (LLMs). It equips students with an essential understanding of transformers, attention mechanisms, embedding techniques, mixture of experts (MoE), fine-tuning techniques, diffusion models, speech recognition and computer vision. The programme also covers the use of computational tools and software for GenAI and LLMs, and explores their applications in financial technology (FinTech) and business contexts.

The programme enables students to analyse finance and business operations and apply automation techniques using Power Platform. It focuses on harnessing Power Query, Power Automate, and Power BI to automate data refresh processes and develop dynamic dashboards to streamline finance and business processes.

The programme provides contemporary knowledge in engineering economics and financial analytics to evaluate project engineering decisions. To perform quantitative analysis practically, students will master the intersection of economic analysis, financial analysis, capital budgeting, linear programming, optimisation, and data analytics. It also explores how the applications of artificial intelligence (AI) and machine learning (ML) support economic analysis and financial analytics.

The programme provides a contemporary foundation in stochastic processes and financial analytics. Students will master essential concepts in probability, statistics, stochastic processes, and financial modelling, and learn to apply computational tools and software and numerical methods for pricing assets and derivatives.

The programme aims to provide students with contemporary knowledge of decentralized finance (DeFi), virtual assets and algorithmic trading (algo trading). It equips students with the ability to read and interpret altcoin whitepapers as a company introduction, understand how these projects position themselves within the blockchain ecosystem, and analyse how their tokenomics support functionality and growth. Students will also use computational tools and software in data analysis, portfolio optimisation, backtesting, algo trading strategies and practical implementation of automated trading systems.