"Probability of Default (PD): The Core of Credit Risk Modeling"

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What is the chance that a borrower will default on a loan?
That’s exactly what Probability of Default (PD) answers — and it’s the foundation of all credit risk models.

PD is the likelihood that a borrower won’t meet their debt obligations within a specific time frame, usually one year. Whether you’re a bank, fintech, or investor, understanding PD helps you price loans accurately, manage risk proactively, and comply with regulatory frameworks like Basel II/III.

📊 Types of PD Estimates:
Point-in-Time (PIT): Reflects current credit conditions (short-term view)

Through-the-Cycle (TTC): Averages PD over time (long-term, stable view)

🔍 How Is PD Estimated?
Statistical Models: Logistic regression, decision trees, MDA, etc.

Structural Models: Like the Merton Model using market data

Expert Scorecards: Rule-based models for retail credit

Credit Ratings: External (Moody’s, S&P) or internal bank ratings

✅ Why PD Matters:
Core component of Expected Credit Loss (ECL):
ECL = PD × LGD × EAD

Helps in loan pricing, provisioning, and capital allocation

Used in IFRS 9, Basel III, and stress testing

Whether you're building a retail scorecard, assessing SME risk, or analyzing corporate bonds, PD is the number you can’t afford to ignore.

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