Overview
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This specialization provides a comprehensive pathway to mastering credit risk modeling from theory to practical application. Learners will explore key concepts such as Probability of Default (PD), Loss Given Default (LGD), and Expected Loss (EL), progressing to advanced frameworks like the Altman Z-Score and Merton’s Model. Through sector-specific and real-world case studies, participants will learn to assess financial statements, assign credit ratings, and build robust risk models aligned with banking and regulatory standards. Designed for finance professionals and analysts, this specialization bridges data-driven analysis with decision-making proficiency in corporate and institutional credit risk.
Syllabus
- Course 1: Advanced Credit Risk Modeling - IT Sector
- Course 2: Credit Risk Modeling
- Course 3: Credit Risk Modeling & its Application in Banks
Courses
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Build advanced credit risk modeling skills by analyzing financial statements, comparing corporate credit profiles, and developing evidence-based recommendations. Designed for learners seeking deeper expertise in corporate credit analysis, this course uses financial data from two companies to connect financial statement analysis with structural credit risk models. You’ll examine income statements, balance sheets, and cash flow statements; evaluate multi-year trends in profitability, efficiency, liquidity, and financial health; and compare company performance using key ratios and evaluation metrics. You’ll apply the Altman Z-Score to assess bankruptcy risk and the Merton Model to evaluate market-based credit risk through asset volatility and distance to default. You’ll also construct unlevered free cash flow to equity (UFCE) models and analyze working capital movements to assess internal cash generation and operational sustainability. What makes this course distinctive is its integrated approach: you’ll combine financial and market-based analysis to assign internal credit ratings, formulate risk-adjusted exit strategies, and produce comprehensive credit recommendations. Enroll to strengthen your ability to interpret financial data, evaluate corporate creditworthiness, synthesize risk findings, and make well-supported credit decisions.
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Build practical credit risk modeling skills and learn how financial institutions evaluate and manage borrower risk. Designed for aspiring risk analysts, finance professionals, banking practitioners, and advanced finance students, this course develops your understanding of Probability of Default (PD), Loss Given Default (LGD), Expected Loss (EL), and structural and reduced-form credit risk models. You’ll apply the Altman Z-Score to assess bankruptcy risk, interpret credit ratings and evaluation metrics, and examine airline industry cases to identify credit signals and evaluate financial health. You’ll also analyze financial statements, working capital needs, and unhedged foreign currency exposure (UFCE), then use these insights to assess borrower exposure and determine appropriate credit structures. As you progress, you’ll explore internal rating systems, red-flag indicators, parent support structures, and lender “ways out” strategies. By the end, you’ll be able to compare credit models, evaluate corporate creditworthiness, and construct an internal risk assessment framework. The course’s structured progression—from foundational models and numerical examples to industry cases and institutional lending practices—helps you connect financial analysis with evidence-based credit decisions. Enroll to strengthen your ability to assess credit risk using established, industry-relevant techniques.
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Build a practical foundation in credit risk modeling and learn how banks and financial institutions measure, interpret, and evaluate credit risk. Designed for learners interested in banking and financial services, this course introduces the concepts, calculations, and challenges that support risk-based decision-making. You’ll explore the purpose and structure of credit risk frameworks and examine why credit risk has become increasingly important following financial crises. Through step-by-step explanations and real-world examples, you’ll analyze Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD). You’ll calculate expected credit loss, distinguish between settlement and pre-settlement risk, and evaluate how model assumptions and data limitations affect risk assessments. The course also connects modeling outputs with capital adequacy and regulatory requirements, helping you understand their role in institutional risk management. Its focused progression from foundational concepts to quantitative estimation and practical evaluation makes complex risk metrics easier to apply. Enroll to build the knowledge needed to interpret credit risk measures, assess model limitations, and evaluate credit risk models in real-world banking contexts.
Taught by
EDUCBA