Financial Modeling for Credit Risk Analysis Course

Lesson 1 • Regulatory and Accounting Context

Surveys capital adequacy frameworks and expected-loss accounting standards that drive credit model requirements. Grounds modeling choices in real institutional constraints.

Lesson 2 • Credit Risk in Financial Markets

Examines how credit risk manifests across loan portfolios, bonds, and derivatives. Connects market context to the analytical decisions modelers must make.

Lesson 3 • Core Concepts of Credit Risk

Defines credit risk, its components, and its role in lending and investment decisions. Establishes the conceptual baseline for every modeling technique introduced later.

Lesson 1 • Model Validation and Error Checking

Teaches systematic techniques to detect formula errors, circular references, and logical inconsistencies. Validation routines are embedded directly into the model template.

Lesson 2 • Model Design Principles

Introduces separation of inputs, calculations, and outputs as the foundation of robust model architecture. Applies these principles directly to credit risk contexts.

Lesson 3 • Building a Model Template

Guides construction of a reusable credit model shell with standardized sections. The template becomes the structural foundation for all models built in later chapters.

Lesson 1 • Reading Financial Statements Critically

Trains analysts to identify accounting choices that distort credit-relevant metrics. Builds the skeptical reading habit required before any ratio or model work.

Lesson 2 • Cash Flow Analysis for Debt Service

Distinguishes operating, investing, and financing cash flows and derives free cash flow available for debt repayment. Establishes the primary repayment analysis used in credit decisions.

Lesson 3 • Spreading Financial Statements

Builds a multi-year spreading template that standardizes financials across borrowers and periods. The spread output feeds directly into the cash flow and debt capacity models.

Lesson 4 • Credit-Focused Ratio Analysis

Calculates and interprets leverage, coverage, liquidity, and profitability ratios from a lender's perspective. Ratios are benchmarked against industry norms and covenant thresholds.

Lesson 1 • Model Performance and Validation

Applies discrimination and calibration metrics to assess PD model quality. Validation results determine whether a model is fit for regulatory and business use.

Lesson 2 • Conceptual Frameworks for PD Estimation

Contrasts structural, reduced-form, and empirical approaches to default probability. Provides the theoretical grounding needed to select and justify a modeling approach.

Lesson 3 • Logistic Regression for Default Prediction

Constructs a logistic regression model using financial and non-financial predictors of default. Covers variable selection, coefficient interpretation, and model fit diagnostics.

Lesson 1 • Expected Loss Computation

Integrates PD, LGD, and EAD into a unified expected loss formula at the facility and portfolio level. EL outputs are linked to pricing, provisioning, and capital allocation decisions.

Lesson 2 • LGD Model Construction

Builds regression-based and workout LGD models using historical default and recovery data. Covers downturn LGD adjustments required under capital adequacy frameworks.

Lesson 3 • Collateral Valuation and Recovery Analysis

Estimates recoverable value from collateral under stressed liquidation scenarios. Recovery rates feed directly into LGD calculations for secured credit facilities.

Lesson 1 • Quantitative Scoring Components

Builds the financial ratio and cash flow scoring modules that form the quantitative pillar of the rating model. Each factor is weighted based on predictive power for default.

Lesson 2 • Rating Model Output and Application

Maps composite scores to rating grades and associated PD ranges on a master scale. Rating outputs drive credit approval, pricing, and portfolio monitoring decisions.

Lesson 3 • Internal Rating System Design

Defines the architecture of a dual-risk rating system covering borrower and facility dimensions. Design choices are linked to regulatory expectations for internal ratings-based approaches.

Lesson 1 • Macroeconomic Scenario Construction

Builds baseline, adverse, and severely adverse macroeconomic scenarios using key economic variables. Scenarios are translated into credit parameter shocks for model inputs.

Lesson 2 • Portfolio Stress Test Execution

Runs stressed parameters through the portfolio model to produce loss distributions under each scenario. Results are aggregated by segment, sector, and rating grade.

Lesson 3 • Communicating Stress Test Results

Structures stress test findings into executive summaries and regulatory submission formats. Visualization techniques make scenario impacts clear to non-technical stakeholders.

Lesson 4 • Linking Macro Scenarios to Credit Parameters

Establishes statistical relationships between macroeconomic variables and PD, LGD, and EAD. Satellite models translate scenario shocks into stressed credit parameter estimates.

Lesson 1 • Economic Capital Frameworks

Defines economic capital as the buffer against unexpected credit losses at a target confidence level. Compares economic capital to regulatory capital and explains the gap.

Lesson 2 • Concentration Risk Management

Quantifies single-name, sector, and geographic concentrations and their impact on unexpected loss. Limit frameworks and hedging strategies are evaluated as concentration controls.

Lesson 3 • Portfolio Credit Risk Concepts

Introduces concentration risk, correlation, and diversification effects that distinguish portfolio risk from single-name risk. Establishes the conceptual basis for portfolio-level modeling.

Lesson 4 • Credit Loss Distribution Modeling

Constructs the credit loss distribution using Monte Carlo simulation and analytical approximations. The distribution reveals expected loss, unexpected loss, and tail risk simultaneously.