🧮 AI for Credit, Market, and Operational Risk

Financial institutions are using AI to analyse risk, monitor changing conditions and support decisions across increasingly interconnected financial systems. AI for Credit, Market and Operational Risk explores these applications alongside the controls needed to assess their reliability and manage their limitations.

Across nine lessons, you will examine credit risk modelling, including probability of default, loss given default and exposure at default; market volatility and portfolio sensitivity; liquidity and balance sheet dynamics; and operational risk monitoring. The course also covers emerging climate, geopolitical, concentration and systemic risks, explaining how AI can support enterprise risk integration and early warning systems.

You will explore machine learning, predictive analytics, generative AI and emerging AI agents, with practical examples that connect analytical outputs to risk management decisions. Particular attention is given to data quality, uncertainty, scenario analysis and the distinction between identifying patterns and establishing reliable evidence.

Governance runs throughout the course, including independent validation, explainability, fairness, model risk management, third party dependencies and meaningful human oversight. The final lesson examines how institutions build an AI enabled risk function through clear ownership, skilled people, controlled implementation and ongoing monitoring.

Designed for risk professionals, financial services practitioners, governance specialists and senior decision makers, this course provides a practical, globally relevant foundation. By the end, you will understand where AI can add value, how to challenge its outputs and what organisational capabilities are needed to use it responsibly within applicable regulatory requirements.