💣 Loan Loss Provisioning and Credit Risk Assessment: From Bad Loans to Smart Boards

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🎯 Setting the Scene: Why Do Banks Fear Defaults?

Imagine you lent your friend \$1,000 and he told you, “Bro, I might pay you back…”
That might is the starting point of credit risk.

Banks face this risk every day—but they don’t just sit and hope for the best. They plan for it. And that plan starts with Loan Loss Provisioning.

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🧱 Loan Loss Provisions and Reserves

So what happens when a bank expects some borrowers to default? They set aside money to absorb that hit. This is called a loan loss provision—kind of like putting bubble wrap around your phone before dropping it.

Over time, as more bubble wrap gets added year after year, you get loan loss reserves.

💡 These reserves are usually counted as Tier 2 Capital—the bank’s backup parachute.

When calculating reserves, banks look at:

• $\textbf{Credit quality policies}$ $($Are we giving loans to the right people?$)$
• $\textbf{Previous losses}$ $($Have we been burned before?$)$
• $\textbf{Loan growth}$ $($Are we throwing money faster than we can track it?$)$
• $\textbf{Manager quality}$ $($Is our lending team more “Wolf of Wall Street” than “Warren Buffett”?$)$
• $\textbf{Collections & recovery}$ policies $($How well do we chase payments?$)$
• $\textbf{Economic trends}$ $($Is the market a sunny day or a thunderstorm?$)$

But here’s a twist—how do banks actually estimate how much they’ll lose?

Let’s move to…

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📊 Expected Loss vs. Unexpected Loss

Say hello to Expected Loss $EL(\$) = PD \times LGD \times EAD$

That’s:

• $PD$: Probability of Default
• $LGD$: Loss Given Default
• $EAD$: Exposure at Default

Let’s decode these using a funny analogy:

You’re lending a car to 3 friends:

• PD: How likely is each friend to crash?
• LGD: If they crash, how badly is the car damaged?
• EAD: How long and how far are they driving?

From the table:

Borrower	PD	LGD	EAD	EL
AAA	0.09%	30%	\$550,000	\$148.50
B+	3.78%	45%	\$785,000	\$13,352.85
Defaulted	100%	100%	\$1,000,000	\$1,000,000

As you can see, higher risk = higher expected loss.

But what about unexpected loss?
It’s like your friend crashing into a spaceship. No one saw that coming. It’s a tail risk—a 99th percentile disaster.

So here’s the question: How do banks formally prepare for these losses in accounting?

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🔍 Expected Loss Under IFRS 9

Enter IFRS 9 : a new sheriff replacing the old “wait-till-it-burns” $($IAS 39$)$ model with a proactive 3-stage model.

🚦 The Three Stages:

1. Stage 1 – Performing Loans
   → Use 12-month expected loss on gross amount.
   🟢 Think of it as: “He’s fine now, but let’s stay cautious.”
2. Stage 2 – Loans Showing Trouble
   → Use lifetime expected loss on gross amount.
   🟠 “Something’s fishy… better brace for impact.”
3. Stage 3 – Nonperforming Loans
   → Use lifetime expected loss on net amount.
   🔴 “Yep. Crash confirmed. Get the airbags!”

Now the next puzzle: What happens to loans that already went bad? Can they still be rescued?

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🛠️ Managing Loss Assets: Keep or Kill?

When things go south, we need a workout—and no, it’s not a treadmill. It’s a loan workout procedure, which includes:

1. Collect extra collateral or capital
2. Help borrower improve business
3. Bring in a joint venture or buyer
4. Liquidate the asset out of court

Now comes the fork in the road:

🧊 Option 1: Retain the Loss Asset

• Wait longer, hope for recovery
• Common in UK-style banks
• Keeps reserves larger

🔥 Option 2: Write Off the Loss

• Accept the loss, clean the books
• Common in US-style banks
• Makes reserves look leaner

Which one is better? Depends on whether you’re more “Tea & Patience” 🇬🇧 or “Cut Loss, Move On” 🇺🇸.

But wait—what’s the foundation of these decisions?

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🧠 Credit Risk Analysis

Let’s step back and analyze the battlefield:

🧾 What should the bank track?

• Major loan types $($number of customers, maturity, interest$)$
• Portfolio distribution $($sectors, currency, short-term vs long-term$)$
• Loans with guarantees
• Risk classifications
• Vintage analysis of nonperforming loans

It’s like analyzing your wardrobe:
👕 How many shirts?
📦 How long have you had them?
🧽 Which ones are dirty?
🧵 Which ones are falling apart?

All this helps banks understand where risk is hiding.

So who keeps the tailor’s eye on all this?

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🧑‍⚖️ Credit Risk Management Capacity

Welcome to the boardroom.
The board of directors doesn’t just wear suits—they must ask tough questions:

• Are our loans profitable and collectible?
• Are risks priced appropriately?
• Are income sources stable and diverse?
• Are PD, LGD, EAD estimates accurate?
• Do we stress test regularly?
• How’s our staffing and training?
• Is our information flow clean and fast?

✅ What should the board review?

1. Lending process
   Origination → Appraisal → Approval → Monitoring → Collection
2. Staffing
   Age, experience, training, responsibilities
3. Information flow
   Is key credit data visible to risk committees and the board?

Because a blindfolded captain can’t steer the ship—even if the vessel is strong.

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🔚 Wrapping Up: Risk Isn’t a Monster, It’s a Math Problem

So next time someone says “banks are too cautious,” remember:

They’re juggling:

• Risky borrowers,
• Risky economies,
• Risky forecasts…

…all while calculating $PD$, $LGD$, $EAD$, and provisioning under $IFRS 9$.

Understanding loan loss provisioning is the first step toward understanding how banks survive storms and thrive in calm.