Credit Risk Fundamentals

🏦 Credit Risk Fundamentals: The Art of Lending Wisely

Imagine you’re a kid with a lemonade stand. Your friend asks to borrow $5 and promises to pay you back next week. Should you lend it? That’s credit risk!

🎯 What You’ll Master

By the end of this guide, you’ll understand:

• What credit risk really means
• The three magic numbers banks use (PD, LGD, EAD)
• Expected vs. unexpected losses
• How banks measure credit risk

📖 Credit Risk Overview

The Story of Lending

Picture yourself as a librarian. Every day, people borrow books from you. Most return them on time. Some return them late. A few… never return them at all! 📚

Credit risk works the same way.

When a bank lends money, there’s always a chance the borrower won’t pay it back. That chance? That’s credit risk.

What Exactly Is Credit Risk?

Credit risk = The possibility that a borrower fails to repay what they owe.

Think of it like this:

Why Should We Care?

Banks make money by lending. But if too many people don’t pay back, the bank loses money—and could even collapse!

Real Example: If a bank lends $1 million and 5% of borrowers default, that’s $50,000 lost. Now imagine billions of dollars!

graph TD
    A["Bank Lends Money"] --> B{Borrower Pays Back?}
    B -->|Yes| C["Bank Earns Interest 💰"]
    B -->|No| D["Bank Loses Money 😰"]
    D --> E["Credit Risk Realized"]

🔢 Credit Risk Parameters

Banks don’t just guess who’s risky. They use three magical numbers to measure risk precisely.

Meet the Big Three: PD, LGD, and EAD

1️⃣ PD - Probability of Default

What is it? The chance (in %) that a borrower will fail to pay.

Kid-friendly version: If 10 kids borrow candy and 1 never returns it, the PD is 10%.

Example:

• A borrower with PD = 2% → Out of 100 similar borrowers, 2 might default
• A borrower with PD = 15% → Out of 100 similar borrowers, 15 might default

2️⃣ LGD - Loss Given Default

What is it? If someone defaults, how much of the loan do you actually lose?

Kid-friendly version: You lent $10. They can’t pay all of it, but they give back $4. You lost $6, so LGD = 60%.

Why isn’t it always 100%?

• The borrower might pay back some money
• You might sell their collateral (like a house or car)
• Legal action might recover some funds

Example:

3️⃣ EAD - Exposure at Default

What is it? The total amount the bank could lose at the moment of default.

Kid-friendly version: If your friend can borrow up to $20 from you, and they’ve borrowed $15 when they say “I can’t pay,” then EAD = $15.

Why is this tricky?

• Credit cards have limits, but people don’t always use the full limit
• Lines of credit can be drawn down more over time
• Interest keeps adding up!

Example:

• Credit limit: $10,000
• Current balance: $7,500
• Unused portion might be drawn before default
• EAD might be estimated at $8,500

🧮 The Magic Formula

Here’s how these three work together:

Expected Loss = PD × LGD × EAD

Example Calculation:

• PD = 5% (5% chance of default)
• LGD = 40% (you’d lose 40% if they default)
• EAD = $100,000 (amount at risk)

Expected Loss = 0.05 × 0.40 × $100,000 = $2,000

The bank should set aside $2,000 to cover this potential loss!

graph TD
    A["PD: Will they default?"] --> D["Expected Loss"]
    B["LGD: How much lost?"] --> D
    C["EAD: How much exposed?"] --> D
    D --> E["EL = PD × LGD × EAD"]

⚖️ Expected vs. Unexpected Loss

This is where it gets really interesting!

Expected Loss (EL) - The Known Unknown

What is it? The average loss a bank expects to happen over time.

Kid-friendly version: You run a lemonade stand. Every week, about 2 lemons go bad. That’s expected—you plan for it!

• It’s predictable
• Banks treat it as a cost of doing business
• They cover it by charging higher interest rates

Example: If a bank expects to lose $1 million per year on bad loans, they’ll charge enough interest to cover that $1 million.

Unexpected Loss (UL) - The Surprise Storm

What is it? Losses that are larger than expected—the bad surprises!

Kid-friendly version: Usually 2 lemons go bad per week. But one week, a storm ruins 20 lemons! That’s unexpected!

• It’s unpredictable
• Banks hold capital reserves to survive these shocks
• This is what keeps banks from failing during crises

graph TD
    A["Total Possible Loss"] --> B["Expected Loss"]
    A --> C["Unexpected Loss"]
    B --> D["Covered by: Interest Income 💵"]
    C --> E["Covered by: Capital Reserves 🏦"]

The Bell Curve of Losses

Imagine all possible losses on a chart:

Real Example:

• 2008 Financial Crisis: Banks expected maybe 2-3% defaults on mortgages. Reality? 10-15% in some areas! The “unexpected” loss was massive.

📊 Credit Risk Measurement Models

Banks don’t just guess—they use sophisticated models!

The Three Main Approaches

1️⃣ Standardized Approach

What is it? Using simple, pre-set rules to measure risk.

Kid-friendly version: Like a school grading system where A=4 points, B=3 points, etc. Everyone uses the same rules.

How it works:

• External rating agencies (like Moody’s, S&P) rate borrowers
• Each rating gets a fixed “risk weight”
• Banks apply these weights to calculate capital needed

Example:

• A $1 million loan to an “A” rated company
• Risk weight = 50%
• Risk-weighted amount = $500,000
• Capital needed (at 8%) = $40,000

2️⃣ IRB Approach (Internal Ratings-Based)

What is it? Banks develop their own models to assess risk.

Kid-friendly version: Instead of using the school’s grading, you create your own system based on how well you know each student!

Two versions:

Why use it?

• More accurate for the bank’s specific portfolio
• Can result in lower capital requirements
• Requires sophisticated systems and data

3️⃣ Credit VaR (Value at Risk)

What is it? Estimates the maximum likely loss over a specific time period.

Kid-friendly version: “I’m 99% sure I won’t lose more than $X this month.”

Key concepts:

• Confidence level: Usually 99% or 99.9%
• Time horizon: Often 1 year for credit risk
• VaR number: Maximum loss at that confidence level

Example:

• 99% VaR of $5 million over 1 year means:
• “We’re 99% confident we won’t lose more than $5 million in a year”
• But 1% of the time, we could lose MORE!

graph TD
    A["Credit VaR Model"] --> B["Collects Historical Data"]
    B --> C["Simulates Scenarios"]
    C --> D["Calculates Loss Distribution"]
    D --> E["Reports Maximum Likely Loss"]

Model Comparison

🎓 Putting It All Together

Let’s see everything in action with a real scenario!

Mini Case Study: Bank ABC

Bank ABC has a loan portfolio worth $10 billion.

Step 1: Identify Risk Parameters

• Average PD across portfolio: 3%
• Average LGD: 45%
• Total EAD: $10 billion

Step 2: Calculate Expected Loss

EL = 0.03 × 0.45 × $10B = $135 million

→ Bank charges enough interest to cover this

Step 3: Calculate Unexpected Loss Using internal models, they find:

• 99% VaR = $400 million

→ Bank holds $400M+ in capital reserves

Step 4: Stress Testing What if PD doubles to 6%?

Stressed EL = 0.06 × 0.45 × $10B = $270 million

→ Bank must prove it can survive this scenario

🌟 Key Takeaways

1. Credit Risk = The chance borrowers won’t pay back
2. PD = How likely is default?
3. LGD = How much is lost if default happens?
4. EAD = How much money is at risk?
5. Expected Loss = PD × LGD × EAD (covered by interest)
6. Unexpected Loss = Surprises beyond expected (covered by capital)
7. Models help banks measure risk systematically

💡 Remember This!

“Credit risk management is like wearing a seatbelt. You hope you never need it, but when you do, it saves everything.”

Banks that manage credit risk well:

• Make smarter lending decisions
• Charge appropriate interest rates
• Hold the right amount of capital
• Survive economic storms

You now understand how banks protect themselves—and the economy—from the dangers of lending! 🎉

Next up: Practice these concepts with hands-on simulations!