The aim of this module is to introduce students to the core concepts, tools, and applications of machine learning and artificial intelligence (AI) as they are currently used within the financial services industry. The module focuses on how data-driven techniques support decision-making, automation, and information processing across financial institutions, markets, and financial technology firms. Emphasis is placed on understanding applied use cases and practical implications rather than on advanced mathematical or statistical theory. The module also introduces recent developments in Generative AI (GAI) as they relate to financial services. This coverage is conceptual and evaluative, focusing on current capabilities, use cases, and limitations rather than on technical implementation or system development. Building on students’ prior knowledge of financial institutions, markets, and fintech innovation, the module develops foundational technical literacy in Python for financial data analysis and introduces key supervised and unsupervised learning techniques. Students will work with structured financial data and selected unstructured sources, learning how data preparation, feature engineering, and model evaluation influence analytical outcomes. Throughout the module, financial business problems are used to illustrate how AI and machine learning methods are applied in professional settings. By the end of the module, students will be equipped to engage thoughtfully and responsibly with AI and machine learning tools in modern financial contexts.

The syllabus covers fundamental issues in machine learning applications and financial analytics, including sessions on 'AI and Data in Financial Services' (e.g. Role of data in financial institutions and markets; Overview of artificial intelligence and machine learning; Types of financial data: market, customer, transactional, and textual; Translating financial business problems into analytics and AI tasks), 'Data Preparation and Exploratory Analysis' (e.g. Data cleaning and preprocessing for financial datasets; Exploratory data analysis and visualisation; Feature construction and transformation; Interpreting patterns in financial and economic data), 'Supervised Learning for Financial Decision-Making' (e.g. Classification and regression problems in finance; Classification & Regression models, including logistic regression, decision tree and random forest models; Model training, validation, and performance evaluation; Interpreting predictions and outputs in financial contexts), 'Unsupervised Learning and Segmentation' (e.g. Clustering and pattern discovery; Customer, client, and product segmentation in finance; Dimensionality reduction concepts; Using unsupervised learning to generate managerial insight), 'Generative AI and Emerging AI Tools in Finance' (e.g. Overview of advancements in GAI relevant to financial services; Conceptual understanding of large language models and generative systems; Current applications of GAI in finance, including research support, reporting, compliance, and decision support; Limitations, bias, transparency, and governance considerations in GAI systems; Human oversight, accountability, and professional judgement in AI-supported financial decisions).

On successful completion of this module, students will be able to: Describe how AI and machine learning are applied within modern financial institutions and markets. Implement appropriate data preparation, feature engineering, and model evaluation techniques in financial analytics contexts. Apply supervised and unsupervised machine learning techniques using Python to financial datasets. Analyse the performance and limitations of machine learning models in financial decision-making contexts. Critically evaluate current uses of Generative AI, including RAG and AI agents, in financial services.

On successful completion of this module, students will be able to: Reflect on the ethical, governance, and professional responsibilities associated with the use of AI technologies in financial services.

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The module is delivered through a blended approach combining lectures and practical laboratory sessions. Lectures introduce the core theoretical concepts underpinning machine learning and artificial intelligence in financial contexts, covering key supervised and unsupervised learning techniques and their contemporary applications. Recent developments in AI and finance are embedded throughout the module through the use of current case studies, discussion of emerging research trends, and critical examination of ethical, regulatory, and governance challenges arising from real-world deployment. The student learning experience is active and practice-focused. Lectures are interactive, encouraging discussion, questioning, and critical reflection, particularly regarding the ethical and societal implications of AI in finance. Weekly laboratory sessions provide hands-on experience using Python to implement and analyse the techniques introduced in lectures, enabling students to bridge theory and practice. Peer-to-peer learning is actively encouraged in lab sessions, supporting collaborative problem-solving and shared learning. Structured reflection on these experiences forms part of the module e-tivity, reinforcing learning and self-awareness. The learning environment aligns with UL’s Integrated Curriculum Development Framework by integrating disciplinary knowledge, digital skills, ethical awareness, and reflective learning within an applied and collaborative setting. In the latter part of the module, teaching and learning activities increasingly focus on the final project, with students sharing interim progress, receiving formative feedback from their peers, and having the opportunity to refine their work iteratively. The module supports the development of UL Graduate Attributes. Analytical thinking and problem-solving skills are developed through quantitative modelling and data analysis tasks. Ethical awareness is fostered through critical engagement with governance, bias, and accountability issues in AI-driven financial systems. Digital competence is developed through sustained use of Python and data-driven tools. Collaborative and communication skills are enhanced through peer-to-peer learning, reflective activities, and the final project, which includes both a written report and an oral presentation.