Introduction
Credit risk sensitivity analysis is a critical component of stress testing for banks, ensuring financial institutions can withstand adverse scenarios. The Reserve Bank of India (RBI) mandates banks to assess their credit risk exposure under nine predefined shocks as part of its stress testing framework. These shocks simulate adverse economic conditions and assess their impact on a bank’s asset quality, provisioning requirements, and capital adequacy.
This blog provides a detailed explanation of these nine shocks, their implications, and real-world examples to help banks understand and implement effective stress testing.
1. Increase in Non-Performing Assets (NPAs)
Explanation:
During economic downturns, borrowers may face cash flow constraints, leading to a surge in non-performing assets (NPAs). This shock assesses the impact of a significant increase in NPAs on a bank’s financial stability.
Stress Scenarios:
- Baseline: 50% increase in NPAs
- Medium: 100% increase in NPAs
- Severe: 150% increase in NPAs
Additionally, banks must increase provisioning:
- Standard loans: 1%
- Substandard loans: 30%
- Doubtful loans (over 1 year): 100%
Example:
During the COVID-19 pandemic, businesses faced revenue losses, increasing defaults on loans, especially in MSMEs. If a bank had an NPA level of ₹5,000 crores, a 100% increase in NPAs would push it to ₹10,000 crores, requiring higher provisioning and impacting profitability.
2. Increase in NPA in Top Five Industries
Explanation:
Certain industries are more vulnerable during economic downturns, affecting their debt repayment ability. This shock tests how concentrated exposure in top industries can impact a bank’s asset quality.
Stress Scenarios:
- Baseline: 3% increase in NPAs in top five industries
- Medium: 5% increase in NPAs in top five industries
Example:
If a bank has high exposure to real estate, telecom, power, infrastructure, and steel, an economic slowdown in these sectors can cause a 5% increase in NPAs, forcing the bank to make additional provisions and impacting capital buffers.
3. Increase in NPA in Specific Sectors
Explanation:
Some sectors experience stress due to idiosyncratic risks, such as policy changes, geopolitical issues, or technological disruptions.
Stress Scenarios:
- Baseline: 3% increase in NPAs in specific sectors
- Medium: 5% increase in NPAs in specific sectors
Example:
In 2018, India’s telecom industry faced stress due to intense competition, leading to debt defaults. A bank with a ₹10,000 crore loan book in telecom would need to provision for an extra ₹300-500 crores due to rising NPAs.
4. Slippage of Restructured Standard Assets
Explanation:
Restructured loans are more likely to turn into NPAs under stress. This shock tests the percentage of restructured assets that deteriorate.
Stress Scenarios:
- Baseline: 20% slippage of restructured assets
- Medium: 30% slippage
- Severe: 40% slippage
Example:
A bank that had restructured ₹1,000 crores of loans in the MSME sector may need to recognize ₹400 crores as NPAs under a severe scenario.
5. Depletion in Collateral Value
Explanation:
Asset-backed loans depend on the collateral’s value. A downturn can lead to a decline in collateral value, increasing credit risk.
Stress Scenarios:
- Baseline: 10% decrease in collateral value
- Medium: 15% decrease
- Severe: 20% decrease
Example:
A bank with ₹5,000 crores in mortgage-backed loans could see its collateral value drop by ₹1,000 crores (20% decline), increasing loan-to-value (LTV) ratios and credit risk.
6. Downgrade in Counterparty Rating
Explanation:
During downturns, credit rating agencies downgrade borrowers, leading to increased risk-weighted assets (RWA) and capital requirements.
Stress Scenarios:
- Baseline: 5% downgrade in borrowers
- Medium: 10% downgrade
- Severe: 20% downgrade
Example:
If 10% of a bank’s corporate borrowers are downgraded, the bank’s RWA increases, affecting capital adequacy ratios (CAR).
7. Concentration Risk – Individual Borrowers
Explanation:
Large borrowers defaulting can create significant financial stress.
Stress Scenarios:
- Baseline: Default by top 1 borrower
- Medium: Default by top 2 borrowers
- Severe: Default by top 3 borrowers
Example:
A large corporate default, like IL&FS in 2018, led to cascading effects in the banking sector, impacting liquidity and asset quality.
8. Concentration Risk – Group Borrowers
Explanation:
Conglomerates with multiple entities may see group-wide defaults in stress scenarios.
Stress Scenarios:
- Baseline: Default by top 3 group companies
- Medium: Default by top 5 group companies
- Severe: Default by all group companies
Example:
If a bank has major exposure to Adani Group or Reliance Group, a severe crisis in one company may lead to group-wide defaults, increasing credit risk.
9. Concentration Risk – Industries/Sectors
Explanation:
Certain industries may collapse in a crisis, leading to systemic risk.
Stress Scenarios:
- Baseline: Default in topmost industry/sector
- Medium: Default in top three industries/sectors
- Severe: Default in top five industries/sectors
Example:
If a bank has major lending in the power, infrastructure, and textile industries, a severe economic slowdown may lead to large-scale defaults, increasing NPA levels significantly.
Conclusion
The RBI’s nine predefined credit risk shocks provide a structured approach to evaluating a bank’s credit risk resilience. By implementing robust stress testing mechanisms, banks can proactively assess vulnerabilities, plan capital buffers, and enhance risk management strategies to navigate economic uncertainties.