This comprehensive course equips learners with the knowledge and practical tools to analyze, evaluate, and apply key credit risk modeling techniques used in modern financial institutions. Through a blend of theoretical frameworks and real-world case studies, learners will explore foundational concepts such as Probability of Default (PD), Loss Given Default (LGD), and Expected Loss (EL), progressing into structural models like Merton’s approach and market-based credit assessment methods.

Participants will also construct and interpret Altman Z-scores to assess bankruptcy risk, and apply credit rating principles to real-world scenarios including airline industry case studies. The course further delves into corporate credit evaluation using internal financial metrics, unhedged foreign currency exposure (UFCE), and working capital analysis, concluding with internal rating systems and lender “ways out” strategies. Designed for aspiring risk analysts, finance professionals, and advanced students, this course combines instructional rigor with practical relevance, enabling learners to build, differentiate, and justify credit decisions with confidence.

This course provides a structured and practical introduction to credit risk modeling with a focus on its application in banking and financial institutions. Designed for learners seeking to analyze, calculate, and evaluate core credit risk components, the course begins by establishing a conceptual foundation for credit risk and its growing importance post-financial crises.

Through real-world examples and step-by-step breakdowns, learners will gain a strong grasp of key modeling inputs such as Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). They will also learn how to compute expected loss, differentiate between settlement and pre-settlement risk, and assess the practical challenges that arise due to model assumptions and data limitations. By the end of the course, learners will be equipped to interpret and apply credit risk metrics, support risk-based decision-making, and align modeling outputs with capital adequacy and regulatory requirements in a financial services context.