Financial Technology and Applied AI

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Translate finance problems into appropriate AI tasks, selecting suitable tool types (e.g., LLM, AI agents, rules‑based, RPA, analytics) and designing effective prompting strategies.
• Analyse operational processes to identify automation opportunities and determine where RPA, human review and AI‑assisted judgement should sit in the workflow.
• Create lightweight prototypes and workflow automation designs for realistic financial‑services processes, incorporating appropriate testing, exception handling and controls.
• Evaluate AI solutions using practitioner‑relevant criteria including accuracy, controls, auditability, bias and operational resilience.
• Produce practitioner‑oriented deliverables (e.g., prompt packs, operating procedures, control checklists, business cases) that support auditable and operationally usable AI‑enabled workflows.

Transferable and Generic Skills

Having successfully completed this module you will be able to:

• Work effectively in diverse teams to analyse FinTech cases, develop strategic recommendations and present findings.
• Exercise critical judgement in interpreting AI‑generated content in finance contexts, including checking logic, calculations and alignment with regulatory and ethical expectations.
• Collaborate productively with AI‑enabled tools, including specifying tasks clearly, iterating based on outputs and documenting AI assistance.
• Communicate complex FinTech, data and AI concepts clearly in written and oral form to non‑specialist audiences.
• Reflect on your own learning and evolving career trajectories in FinTech and AI‑enabled finance

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• Explain core AI/ML concepts relevant to financial services (supervised vs unsupervised learning, classification/ranking, forecasting, NLP, generative models and AI agents) at a level sufficient to assess suitability for business problems.
• Summarise key FinTech contexts used in the module (cryptocurrencies, DeFi, open banking/embedded finance, platforms/ecosystems, RegTech/SupTech) and explain how data and APIs enable AI‑supported workflows.
• Describe how AI‑enabled tools are used across financial‑services roles (front office, operations, risk, compliance, product, audit), and distinguish between model‑building, tool use, agentic workflows and process automation.
• Summarise practical governance expectations for AI use in finance confidentiality, documentation, validation, operational resilience and accountability—focusing on implementation in day‑to‑day work.

(12 teaching weeks – double lecture each week): Week 1 – AI in Financial Services: roles, workflows and core AI/ML concepts including AI agents; how this module differs from technical model‑building modules; safe and responsible tool use in finance. Week 2 – Prompt Engineering for Finance: prompt patterns for analyst work (structured outputs, assumptions, uncertainty, sensitivity scenarios); validation of numbers and logic; writing for different stakeholders (clients, risk, audit, regulators). Week 3 – AI‑Assisted Data Work: using AI copilots to support data cleaning, spreadsheet logic, basic SQL/pseudocode and reproducible analysis; turning messy operational data into usable MI (management information). Week 4 – Vibe Coding and Prototyping (Tools & APIs): using AI to prototype small tools (e.g., cash‑flow helpers, reconciliation checks) and working with APIs/data contracts in an Open Banking context; safe testing, validation, and “trust but verify”. Week 5 – Cryptocurrencies and Decentralised Finance (DeFi): core concepts (blockchain, smart contracts, AMMs, stablecoins); contrasting DeFi with TradFi mechanics; AI applications in crypto (on-chain analytics, fraud detection, smart contract auditing/explanation). Week 6 – RPA Fundamentals for Finance Operations: process mapping, exception handling and controls; prototyping RPA for onboarding, KYC file checks, payment exceptions and reporting packs (low‑code/diagram‑based where appropriate); contrasting traditional RPA with emerging AI agent‑based approaches. Week 7 – Document and Knowledge Automation (RegTech/SupTech Context): document triage and extraction, policy/Q&A workflows, regulatory change monitoring; retrieval‑augmented and agent‑based approaches; evaluation of reliability. Week 8 – Customer and Front‑Office Workflows: AI for customer support (including agent‑based interactions), complaints handling, relationship‑manager preparation and personalised communications; guardrails, escalation and quality assurance. Week 9 – Risk and Compliance Workflows: transaction‑monitoring triage, case‑file narrative drafting, audit‑friendly documentation; operational risk and control design for AI + automation. Week 10 – Building the Business Case: benefit sizing, cost/risks, vendor selection, KPI design, and implementation planning for AI and automation in financial services. Week 11 – Governance in Practice: practical artefacts and controls (AI use logs, prompt registers, testing protocols, model/tool risk classification, AI agent oversight, privacy/confidentiality handling) and how to communicate with risk, legal and compliance stakeholders. Risks and ethics in implementing AI in finance. Week 12 – Capstone Demonstrations: group project demos/presentations and integration of course themes; career translation (how to present your prompt packs, prototypes and workflows as employable skills).

Teaching and learning combine conceptual input with applied, career‑relevant activities that focus on FinTech, digital finance and the responsible use of applied AI tools. The module uses: • Lectures introducing key AI/ML concepts, workflow design principles and evaluation frameworks. • Workshop‑style sessions where you design, build and critique AI‑enabled workflows and deliverables using approved tools. • Guided prototyping activities where you use AI assistance to build and test lightweight tools, with emphasis on validation and responsible use. • Process‑automation exercises covering workflow mapping, exception handling and control design. • Applied case studies and role‑plays to develop communication and stakeholder management skills in AI‑enabled work contexts. • Practitioner guest speakers (where available) sharing real‑world experience of AI and automation implementation and governance. • Independent study involving directed reading to deepen understanding and support assessment work. • You are expected to maintain a learning log diary each week, which should form the basis for your individual reflective report.

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Group report and presentation

• Assessment Type: Formative
• Feedback: Oral and written formative feedback on group report and demo/presentation.
• Final Assessment:
• Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.