Credit Risk Modelling: PD, LGD, EAD and Basel III for IIBF Risk Management

Credit risk is at the heart of every lending decision a bank makes. And mastering its quantification is essential for candidates preparing for the IIBF Risk Management certification. This article explains the key concepts — Probability of Default (PD). Loss Given Default (LGD), Exposure at Default (EAD), Expected Loss, Internal Ratings-Based (IRB) approaches, RAROC, and the Basel III framework — with the clarity and depth you need to answer exam questions confidently.

What Is Credit Risk and Why Does It Matter for Banks?

When a bank extends a loan, it faces the possibility that the borrower will not repay. This possibility — credit risk — is the single largest source of risk in commercial banking. Understanding and measuring it precisely is not merely an academic exercise; it determines how much capital a bank must hold. How it prices loans, and whether it remains solvent during economic stress.

The Reserve Bank of India. Following the Basel Committee on Banking Supervision, requires all scheduled commercial banks to maintain capital commensurate with their credit risk exposures. Under Basel III. Indian banks must use approved methodologies to quantify this risk, making the subject directly relevant to your IIBF examination and your professional career.

Credit risk arises from multiple sources:

• Default risk: The borrower fails to meet contractual obligations.
• Migration risk: The borrower's creditworthiness deteriorates, leading to rating downgrades even without an actual default.
• Concentration risk: Excessive exposure to a single borrower, sector, or geography.
• Counterparty risk: Specific to derivatives and off-balance-sheet instruments, where settlement failure is the concern.

For the IIBF Risk Management exam, you must understand all four, but the quantitative framework — PD, LGD, EAD, and Expected Loss — receives the greatest emphasis. Practice on our mock tests to gauge your current understanding before diving deeper.

The Expected Loss Formula: PD, LGD, and EAD Explained

The cornerstone of quantitative credit risk modelling is the Expected Loss (EL) equation:

EL = PD × LGD × EAD

Each component captures a distinct dimension of risk:

Probability of Default (PD)

PD is the likelihood that a borrower will default within a defined time horizon — typically one year. It is expressed as a percentage. For example, a PD of 2% means that, on average, 2 out of every 100 such borrowers are expected to default over the coming year.

PD is derived from historical default data, internal rating systems, or external credit bureau information. Under the Foundation IRB approach. Banks must estimate PD themselves using at least five years of internal data, while the regulator supplies the remaining risk parameters.

Loss Given Default (LGD)

LGD represents the fraction of the exposure that the bank actually loses if the borrower defaults. After accounting for the recovery of collateral, guarantees, and any other credit enhancements. It is expressed as a percentage of EAD.

If a bank recovers 40% of an exposure after a default, the LGD is 60%. Factors influencing LGD include the quality and liquidity of collateral. Seniority of the claim, and efficiency of the legal recovery process — all of which vary significantly across Indian borrower segments.

Exposure at Default (EAD)

EAD is the total outstanding amount the bank is exposed to at the moment of default. For a term loan, EAD roughly equals the outstanding principal. For a revolving credit facility or a letter of credit. EAD is less straightforward — the borrower may draw down more of an unused facility as financial distress increases. Basel III uses a Credit Conversion Factor (CCF) to estimate the likely draw-down for off-balance-sheet items.

A Worked Example

Suppose a bank has granted a working capital loan of ₹50 lakh to a mid-sized manufacturing company. After internal analysis:

• PD = 3% (based on the borrower's internal rating)
• LGD = 45% (collateral is factory machinery, partial recovery expected)
• EAD = ₹50 lakh (fully drawn term facility)

Expected Loss = 0.03 × 0.45 × ₹50,00,000 = ₹67,500

This ₹67,500 represents the average annual loss the bank should statistically provision for on this single exposure. Banks aggregate EL across their entire portfolio to set provisioning levels and pricing floors.

Standardised vs IRB Approaches Under Basel III

Basel III offers banks a menu of approaches of increasing sophistication for calculating credit risk capital requirements. Choosing among them involves a trade-off between regulatory prescription and capital efficiency.

The Standardised Approach (SA)

Under the Standardised Approach. Risk weights are fixed by the regulator and depend on the asset class and, where applicable, the external credit rating of the counterparty assigned by an approved External Credit Assessment Institution (ECAI) such as CRISIL, ICRA, or CARE. A corporate rated AAA attracts a lower risk weight than an unrated one. SA is simpler to implement but often less risk-sensitive, meaning it may require more capital than is warranted for a well-collateralised, high-quality borrower.

Foundation Internal Ratings-Based (F-IRB) Approach

Under F-IRB, banks use their own internal estimates of PD but rely on regulatory supervisory values for LGD and EAD. This demands robust internal rating systems validated over several years. RBI approval is required before a bank can adopt IRB. And the bank must demonstrate that ratings meaningfully differentiate risk and are used in internal credit decisions — the "use test."

Advanced Internal Ratings-Based (A-IRB) Approach

A-IRB gives banks the greatest flexibility: they estimate PD, LGD, and EAD themselves, subject to rigorous supervisory validation. This approach is the most data-intensive but typically yields the most risk-sensitive and capital-efficient outcomes for banks with sophisticated risk management infrastructure. The capital formula under IRB is derived from the Asymptotic Single Risk Factor (ASRF) model. Which accounts for systematic economic conditions that drive correlated defaults across the portfolio.

For the IIBF Risk Management exam, be prepared to compare these three approaches across dimensions of complexity, data requirements, regulatory oversight, and capital impact. You can test this knowledge with our risk concept matching game or browse RBI rate resources for current context.

RAROC, Economic Capital, and Regulatory Capital

Beyond measuring expected losses, banks must also understand unexpected losses — the excess loss over and above EL that occurs in adverse scenarios. This is the domain of economic capital and the risk-adjusted return framework.

Regulatory Capital vs Economic Capital

Regulatory capital is the minimum capital a bank must hold as mandated by RBI under Basel III — expressed as a percentage of Risk-Weighted Assets (RWA). Under Basel III. Indian banks must maintain a minimum Total Capital Ratio of 9% (higher than the Basel minimum of 8%), plus a Capital Conservation Buffer of 2.5%, giving an effective floor of 11.5%.

Economic capital is the bank's own internal estimate of the capital needed to absorb unexpected losses at a chosen confidence level (typically 99.9%). It is derived from Value-at-Risk (VaR) models calibrated on the bank's actual portfolio. And it may be higher or lower than regulatory capital depending on the bank's risk profile and the sophistication of its internal models.

Risk-Adjusted Return on Capital (RAROC)

RAROC is the primary tool for risk-adjusted performance measurement. It answers a simple but powerful question: after accounting for expected losses and the cost of holding capital against unexpected losses, is this loan profitable?

RAROC = (Net Revenue − Expected Loss) / Economic Capital

A RAROC above the bank's hurdle rate (its cost of equity) indicates value creation; below it, the loan destroys shareholder value. RAROC allows banks to compare products, business units, and customer relationships on a like-for-like basis, stripping out the distorting effect of leverage. For IIBF candidates pursuing the Risk Management certification, demonstrating an understanding of RAROC and its application in loan pricing is a high-value exam skill.

Key Basel III Credit Risk Enhancements

Basel III strengthened the credit risk framework in several important ways compared to its predecessor, Basel II:

1. Higher and better-quality capital: Common Equity Tier 1 (CET1) must be at least 4.5% of RWA, ensuring that capital is genuinely loss-absorbing.
2. Capital Conservation Buffer: An additional 2.5% CET1 buffer that restricts dividend payments and bonuses when breached.
3. Countercyclical Capital Buffer (CCyB): A regulator-imposed buffer (0–2.5% of RWA) activated during credit booms to build resilience before downturns.
4. Leverage Ratio: A simple non-risk-based backstop requiring Tier 1 capital to be at least 3% of total exposures, preventing excessive balance-sheet build-up.
5. Improved counterparty credit risk standards: Particularly for over-the-counter (OTC) derivatives, including the Credit Valuation Adjustment (CVA) capital charge.

Stay updated on the latest regulatory developments through our IIBF news resource hub. Candidates preparing for JAIIB should also review the foundational principles at our JAIIB course page, while CAIIB candidates can explore advanced topics at our CAIIB course page.

Rating Models and the Credit Risk Modelling Toolkit

Internal rating systems are the foundation on which IRB approaches rest. A well-designed rating model assigns every borrower to a grade that corresponds to a specific PD band. Enabling consistent and auditable credit decisions across the bank.

Indian banks typically employ a combination of the following rating model types:

• Statistical scorecard models: Logistic regression or discriminant analysis applied to financial ratios (current ratio, debt-service coverage, interest coverage) to produce a score that maps to a PD. These are transparent, back-testable, and preferred by regulators.
• Judgmental expert models: Credit committees apply structured criteria, particularly for large, complex borrowers where quantitative data alone is insufficient.
• Structural models (Merton-type): Treat equity as a call option on the firm's assets; default occurs when asset value falls below debt. Used primarily for publicly listed companies where market data is available.
• Reduced-form (intensity) models: Treat default as a statistical process governed by a hazard rate; used widely in bond pricing and derivatives.

The RBI's supervisory review process (Pillar 2 of Basel III) expects banks to validate their rating models regularly through back-testing, benchmarking against external ratings, and stress-testing PD estimates under adverse economic scenarios. Candidates can browse the IIBF blog for more on model validation and risk governance best practices.

Frequently Asked Questions

Mastering credit risk modelling — from the PD × LGD × EAD formula to IRB approaches, RAROC, and Basel III capital buffers — is essential for the IIBF Risk Management certification and for a rewarding career in bank risk management. The international regulatory framework, anchored by the Bank for International Settlements, continues to evolve, so staying current is as important as understanding the fundamentals. Visit the Bank for International Settlements (BIS) for the latest Basel Committee publications. Ready to test your knowledge? Attempt our IIBF Risk Management mock tests now and identify the gaps before exam day.