RBI’s Expected Credit Loss Rule Explained & IMF’s Global Stability Warning — The Calm Before the Storm

Welcome back to Storyantra.in, where we break down complex stories into something clear, meaningful, and just a bit more human.

In today’s edition, we explore two big stories that could shape the world of finance in very different ways.
First — the RBI’s major new rule to prevent bad loans from crippling banks.
And second — the IMF’s latest Global Financial Stability Report, which quietly warns that the calm we see in global markets may just be the silence before a storm.

Story 1: RBI’s New Rule — How Banks Will Now Predict Bad Loans

At its heart, the business of banking is pretty simple.

Banks take deposits from people, pay them a small interest, and then lend that money to others at higher interest rates. The difference becomes their profit.

But here’s the twist — this profit only holds as long as borrowers repay their loans. When loans go unpaid, they turn into bad loans or NPAs (Non-Performing Assets), and banks lose money.

To handle these risks, banks keep aside a part of their profits as provisions — a financial safety cushion for future losses. But the way these provisions are calculated can make or break a bank.

Until now, Indian banks used what’s called the “incurred loss” model, which means they only recognized losses after they actually occurred.
The RBI now wants to change that — by introducing a forward-looking system called the Expected Credit Loss (ECL) framework.

So, what’s changing — and why does it matter?

Under the old method, banks would wait for a loan to show signs of trouble before setting money aside.
For instance:

• If a loan was overdue by 0–30 days, it was labeled SMA-0.
• 31–60 days overdue became SMA-1.
• 61–90 days overdue was SMA-2.

Only when it crossed 90 days did it officially become a Non-Performing Asset — and that’s when banks started taking it seriously.

This reactive approach had one major flaw — by the time banks recognized the problem, the damage was often already done. It was, as analysts say, “too little, too late.”

The ECL model: Looking ahead instead of looking back

RBI’s proposed Expected Credit Loss model flips the system. Instead of waiting for defaults to happen, it tells banks to anticipate losses in advance.

The logic is simple: predict, prepare, and protect.

Banks will now estimate three key factors for every loan:

1. Probability of Default (PD) – What’s the chance this borrower won’t repay?
2. Loss Given Default (LGD) – If they default, how much of the loan is likely lost?
3. Exposure at Default (EAD) – How much money is actually at stake?

Put together, these give a simple formula:
Expected Loss = PD × LGD × EAD

So, even if a borrower is paying on time, the bank will still set aside a small reserve for potential future trouble. And if the borrower’s risk increases, the reserve must rise too — no more waiting for a full default.

Three stages of risk

Under this new model, loans are classified into three stages:

• Stage 1 – Loans that are healthy. Banks will set aside funds for just 12 months of expected losses.
• Stage 2 – Loans that show rising risk but haven’t defaulted yet. Banks must now provision for lifetime expected losses — a much higher reserve.
• Stage 3 – Loans that have defaulted — similar to NPAs today.

Here’s how big the difference can be:
A loan that’s 60 days overdue today might require just 0.25–4% provision. Under ECL, it could jump to 5% or more — that’s a tenfold increase.

But there’s a twist

ECL gives banks more power — and with that, more responsibility.

Under the old system, provisioning was formula-based.
Under ECL, banks have to build their own risk models, predict defaults, and even use forward-looking data like economic forecasts.

This flexibility can work both ways.

• A diligent bank can use this to be more conservative and safer.
• But a careless or manipulative one could tweak assumptions to make its books look better than they are.

That’s why RBI plans to add “regulatory backstops” — minimum provisioning levels to prevent underreporting. For example, even if a bank’s model predicts only 2% loss, it must still provision at least 5% for Stage 2 loans.

The bigger picture

India’s move toward ECL aligns with global standards like IFRS 9 (used in Europe) and the CECL model (in the U.S.).
After the 2008 financial crisis, both were introduced to make banks acknowledge losses earlier and avoid sudden collapses.

So why didn’t India do this sooner?
Because back in the mid-2010s, Indian banks were already struggling with massive NPAs. Introducing stricter provisioning then could have broken them.

Now, with most banks in better shape, the timing makes sense. RBI plans to implement ECL from April 2027, giving banks 1.5 years to prepare.

What it means for you and me

For customers — a bank that provisions more is a safer bank.
For investors — this change will make Indian banks easier to compare with global peers and bring more transparency.

Yes, in the short run, banks’ profits might dip due to higher provisioning, but in the long run, this makes the entire system sturdier and more trustworthy.

In short, the rules of Indian banking are evolving — and that’s a good thing.

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Story 2: IMF’s Global Financial Stability Report — Calm on the Surface, Shaky Beneath

The International Monetary Fund (IMF) just released its latest Global Financial Stability Report, titled “Shifting Ground Beneath the Calm.”

At first glance, everything looks fine. Markets are calm, volatility is down, and investors seem optimistic. But, as the IMF warns, the stability we see might be masking deep cracks below the surface.

The illusion of calm

Stock markets have been rallying since early 2025. The S&P 500’s forward PE ratio is at its 96th percentile — levels last seen during the dotcom bubble.
A huge chunk of this rise comes from the “Magnificent Seven” — U.S. tech giants in the AI and IT sectors.

But here’s the catch: profits aren’t rising fast enough to justify these sky-high prices.
If AI takes longer to become profitable, these overvalued stocks could tumble sharply — triggering a market correction that could ripple across the globe.

Bond markets are uneasy too

While inflation seems under control, yields on government bonds in advanced economies are still climbing.
Why? Because governments are borrowing more while growing slower — a recipe for fiscal stress.

Investors now demand higher returns to lend long-term — something called a “term premium.” In plain terms, they’re saying: “If you want us to fund your 30-year projects, you’ll have to pay more.”

This shows that markets are beginning to lose faith in government discipline. Ironically, private companies are finding it easier to raise funds than governments. But this imbalance can’t last forever.

Emerging markets: the debt dilemma

Emerging markets (EMs) — like India, Brazil, and Malaysia — now hold a record $30 trillion in sovereign debt, around 60% of their GDP on average.

Interestingly, many EMs have started borrowing more in local currency, relying on domestic banks and investors rather than foreign ones.
This shift, called debt localization, makes them more resistant to global shocks — but it also creates a dangerous loop.

When domestic banks hold too much government debt, a weak government hurts the banks, and weak banks hurt the government.
This cycle, known as the “sovereign-bank nexus,” has already caused trouble in countries like Egypt, Pakistan, and Nigeria.

India and China, however, are seen as better equipped to manage these risks due to stronger institutions and deeper local markets.

The hidden fragility of the forex market

Every day, about $9.5 trillion worth of foreign exchange is traded worldwide — making it the most liquid financial market on Earth.
But beneath this size lies fragility.

In recent years, non-bank financial institutions (NBFIs) — hedge funds and trading firms — have taken over much of this trading from traditional banks.
These players use short-term instruments like FX swaps and forwards, which can make the market highly unstable when volatility spikes.

Even small disruptions can now cause chaos. The IMF cited two major trading platforms that went offline briefly — and global liquidity froze almost instantly.
Imagine what could happen if a cyberattack or major dealer default hit the system.

Because the U.S. dollar sits at the center of this web, any stress in dollar funding spreads globally — especially hurting emerging economies that borrow in foreign currency.

The IMF’s message: Don’t wait for the storm

The IMF’s warning is simple — don’t be fooled by the calm.
Now is the time for policymakers to fix the cracks, not when the crisis hits.

• Central banks must keep inflation in check and maintain independence.
• Governments need to control fiscal deficits before markets lose confidence.
• Emerging economies should strengthen local bond markets and reduce overreliance on banks.
• Regulators must stress-test forex markets for liquidity shocks and strengthen cyber defenses.

The big takeaway?
Markets may look calm, but the ground beneath is shifting. The next shock, if ignored, could catch the world completely off guard.

Quick Tidbits

• Nelco, a Tata Power subsidiary with ₹305 crore in revenue, is targeting enterprise and government satellite communication clients, skipping retail due to high terminal costs of ₹10,000–₹15,000.
• France’s CMA CGM has signed a $300 million deal with Cochin Shipyard for six LNG-powered container ships — India’s first global shipbuilding order under a ₹69,725 crore government scheme.
• India’s Central Electricity Regulatory Commission (CERC) plans stricter renewable energy compliance from April 2026. Wind plants may lose up to 48% of revenue and solar 11%, pushing higher investments in battery storage.
• Meanwhile, the CEA has released a master plan for new hydroelectric projects along the Brahmaputra River.

Final Thoughts

Both stories — the RBI’s forward-looking banking reform and the IMF’s quiet warning — point to one truth:
Preparation beats prediction.

Whether it’s banks preparing for bad loans or countries bracing for global shocks, the systems that anticipate risk survive the longest.

Disclaimer: This content is for informational purposes only. 

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