Credit Risk Modelling

Master Credit Risk Modeling: From Default Data Creation to Model Calibration

Are you looking to break into the world of credit risk modeling? Whether you're a student, analyst, or data scientist wanting to specialize in financial risk, this is your complete, hands-on guide to building IRB-compliant TTC PD models — a key regulatory requirement under Basel norms.

What You Will Learn:

1. Introduction to Credit Risk Modeling

• Role of PD models in banking and finance
• Regulatory background: Basel II/III/IV, IRB vs. Standardized approach
• TTC vs. PIT (Point-in-Time) PDs: key differences and use cases

2. Creating Default Data from Scratch

• What constitutes a default under Basel IRB rules
• Data sourcing strategies: internal bank data, financial data , external rating data
• Constructing the default flag: 90 days past due, insolvency, restructuring triggers,written off
• Time Series data structure for tracking credit behavior over time

3. Financial Statement Analysis for Risk Modeling

• Extracting and cleaning Balance Sheet, Income Statement, and Cash Flow data
• Creating meaningful financial ratios:
• Liquidity ratios: Current Ratio, Quick Ratio
• Profitability ratios: ROA, ROE, EBITDA Margin
• Leverage ratios: Debt-to-Equity, Interest Coverage
• Efficiency ratios: Asset Turnover, Inventory Days
• Covering ratios: Ebitda to Total Debt

4. Data Preprocessing and Feature Engineering

• Handling missing data and outliers
• Normalization vs. standardization
• Binning and transformation techniques or Percentile Rank Creation

5. Model Development (TTC PD)

• Logistic regression: the core model for PD estimation
• Model specification aligned with economic cycles
• Segmentation strategies: SME vs. Corporate vs. Retail: Industry Level, Sales Segment
• Benchmarking and variable selection techniques

6. Calibration and Validation

• TTC calibration techniques (e.g., long-run average default rates)
• Aligning model output to regulatory conservatism
• Backtesting, Gini/AUC, KS statistics
• Stress testing and sensitivity analysis

7. IRB Compliance and Documentation

• Key requirements under Basel for IRB approval
• Validation and governance framework
• Model documentation best practices
• Audit trail and internal use tests

Why Learn With Me?

• Hands-on modeling with real-world scenarios
• Step-by-step guidance from data creation to regulatory compliance
• Personalized sessions tailored to your learning pace and background
• Support materials, templates, and practice datasets

Who Is This For?

• Risk Analysts & Credit Officers
• Data Scientists & ML Engineers in Finance
• Finance Students preparing for a career in Risk
• Consultants and FinTech professionals