Risk Analysis equips students with modern risk analysis tools, integrating statistical methods, artificial intelligence, financial risk assessment, and sustainability considerations. Students will explore the role of risk analysis in decision-making, using quantitative techniques to assess and mitigate uncertainty across different domains. The financial risk component will focus on market volatility, credit risk, liquidity concerns, and systemic financial threats. Techniques such as Value at Risk (VaR), stress testing, Monte Carlo simulations, and machine learning applications in financial risk assessment will be covered. Beyond finance, students will examine operational, strategic, and emerging risks, including cybersecurity threats and supply chain disruptions. They will also engage with climate risk resilience, exploring extreme weather event modelling, carbon transition risks, and ESG (Environmental, Social, and Governance) risk integration in corporate decision-making. By the end of the module, students will be able to apply data-driven risk assessment methodologies, interpret predictive models, and develop mitigation strategies for financial and non-financial risks.

The module covers fundamental issues in risk assessment, analysis, and decision-making, including 'Introduction to Risk Analysis and Decision Support', 'Statistical Techniques and Bayesian Probability in Risk Modelling', 'Financial Risk Analysis: Market, Credit, Liquidity, and Systemic Risks', 'Predictive Analytics and Machine Learning for Risk Assessment', 'Climate Risk Resilience: Extreme Weather Modelling and Carbon Transition Risks', 'ESG Risk Management and Sustainability Reporting', 'Non-Financial Risks: Operational, Cybersecurity, and Supply Chain Risks', 'Monte Carlo Simulations, Stress Testing, and Risk Optimisation', 'Risk Treatment Strategies and Policy Implications'

On successful completion of this module students should be able to: Describe the role of risk analysis in resolving risk management problems. Apply a suitable decision-making framework to situations with a probability data set. Analyse the roles and requirements of various stakeholders in a risk assessment context. Interpret the application of various statistical techniques.

On successful completion of this module students should be able to: Critically evaluate the impact of various risk treatment strategies. Reflect on the significance and limitations of quantitative analysis.

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The module is delivered through a two-hour lecture and a practical lab. Lectures focus on real-world risk analysis problems and include applied quantitative demonstrations, ensuring students actively engage with contemporary issues in finance, sustainability, climate risk and emerging technologies. Recent research findings and developments are integrated through current academic literature, live examples and case studies drawn from emerging risk research, ensuring strong alignment between teaching and research The lab provides a hands-on learning environment where students apply analytical techniques to realistic risk scenarios using data and quantitative tools. This approach aligns with UL’s Integrated Curriculum Development Framework by integrating theory, practice and reflection. Graduate Attributes are developed through applied problem-solving, critical thinking, quantitative literacy and responsible decision-making, with collaborative activities supporting communication and professional skills.