Credit Risk Modeling & its Application in Banks
About this course
Build a strong foundation in credit risk modeling and learn how key risk metrics are applied in banking and financial institutions. This course provides a structured, practical introduction to the concepts, calculations, and evaluation techniques used to measure and interpret credit risk in a financial services environment. You will begin by exploring the fundamentals of credit risk and its growing importance following financial crises. Through step-by-step explanations and real-world examples, you will learn how to analyze the core components of credit risk modeling, including Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). You will also calculate Expected Loss, distinguish between settlement and pre-settlement risk, and evaluate the impact of model assumptions and data limitations on risk assessment. Designed for learners interested in banking and financial services, this course helps you interpret credit risk metrics, support risk-based decision-making, and understand how modeling outputs align with capital adequacy and regulatory requirements. By focusing on both conceptual understanding and practical application, the course equips you with the knowledge needed to analyze and evaluate credit risk models in real-world banking contexts.
75/100
CourseAsk score
- What the provider tells you
- 39/45
- Who stands behind it
- 20/35
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- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- analyze Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD)
- calculate Expected Loss and distinguish between settlement and pre-settlement risk
- evaluate the impact of model assumptions and data limitations on risk assessment
Course objectives
- build a strong foundation in credit risk modeling
- support risk-based decision-making in banking
- interpret credit risk metrics effectively
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