Credit Risk PD LGD EAD Model: IIBF Risk Exam Guide

Credit risk is the single largest risk a bank carries, and quantifying it precisely is the backbone of the IIBF Risk Management certificate. The credit risk PD LGD EAD model — built on Probability of Default, Loss Given Default, and Exposure at Default — is the framework banks use to estimate expected loss and set capital under the Basel internal-ratings-based approach. For exam candidates, the credit risk PD LGD EAD model ties together rating systems, provisioning, and capital in one coherent equation. This guide explains each parameter, how they combine into expected and unexpected loss, and the exam-relevant nuances you must master.

The Expected Loss Equation

At the heart of the credit risk PD LGD EAD model lies a deceptively simple formula: Expected Loss (EL) = PD × LGD × EAD. Each term answers a distinct question. Probability of Default (PD) asks: how likely is this borrower to default over a one-year horizon? Loss Given Default (LGD) asks: if default occurs, what fraction of the exposure will the bank actually lose after recoveries and collateral? Exposure at Default (EAD) asks: how much will the bank be owed at the moment of default?

Multiplying the three yields the expected loss — the average loss the bank anticipates and should price into the loan and cover through provisions. For example, a PD of 2%, an LGD of 40%, and an EAD of ₹1 crore gives an expected loss of ₹80,000.

Expected loss is only part of the story. Banks also estimate unexpected loss — the volatility around the average — which capital, not provisions, must absorb. This distinction between EL (covered by provisions/pricing) and UL (covered by capital) is a favourite exam theme, and the structured walkthroughs in the CAIIB risk management modules reinforce the logic well.

Probability of Default and Rating Systems

PD is the cornerstone of the credit risk PD LGD EAD model. It is the likelihood that a borrower fails to meet obligations over a defined horizon, usually one year. Banks estimate PD through internal rating systems that map borrowers to rating grades, each grade carrying a calibrated default probability derived from historical default data.

Two PD concepts matter for the exam. A "through-the-cycle" PD averages default likelihood across economic cycles for stability, while a "point-in-time" PD reflects current conditions and is more volatile. Under the Basel Foundation Internal Ratings-Based (F-IRB) approach, banks estimate their own PD but use supervisory values for LGD and EAD; under the Advanced IRB (A-IRB) approach, they estimate all three parameters internally, subject to regulatory validation.

Rating models combine financial ratios, qualitative factors, and behavioural data, and must be back-tested and validated regularly to ensure calibrated grades actually predict observed defaults. To lock in the F-IRB versus A-IRB distinction and the PD terminology, the quick-fire questions on the IIBF practice tests are an efficient way to test recall before the exam.

LGD, EAD and Risk Mitigation

Loss Given Default measures severity. Expressed as a percentage of exposure, LGD equals one minus the recovery rate. If a bank expects to recover 60% of an exposure through collateral, guarantees, and workout, LGD is 40%. The quality and liquidity of collateral, the seniority of the claim, and the legal enforceability of security all drive LGD lower, which is why credit risk mitigation techniques are so valuable within the credit risk PD LGD EAD model.

Exposure at Default captures the outstanding amount likely to be owed when default occurs. For a term loan this is close to the drawn balance, but for revolving facilities like overdrafts and credit cards, EAD must account for likely additional drawdowns before default — captured through a Credit Conversion Factor (CCF) applied to the undrawn commitment.

Effective mitigation — eligible financial collateral, on-balance-sheet netting, guarantees, and credit derivatives — reduces either LGD or EAD and thus expected loss and capital. Memorising which technique affects which parameter is exam gold, and the matching drills on the IIBF concept game make these mappings stick quickly.

From Parameters to Capital and RAROC

The credit risk PD LGD EAD model ultimately feeds capital and performance measurement. Under the IRB approach, the three parameters enter a Basel-prescribed risk-weight function (which also factors correlation and a maturity adjustment) to compute risk-weighted assets and hence the regulatory capital a bank must hold against unexpected loss. Higher PD, LGD, or EAD all push capital requirements up.

These same parameters drive Risk-Adjusted Return on Capital (RAROC) — a performance metric where risk-adjusted income (net of expected loss) is divided by economic capital. RAROC lets a bank compare the true profitability of loans with very different risk profiles and price loans so that riskier exposures earn commensurately more. It is central to risk-based pricing and capital allocation. Candidates should follow framework updates via the IIBF regulatory updates feed, and consult the authoritative Basel framework published by the Bank for International Settlements for the precise risk-weight formulas. You can also test broader concepts on the IIBF mock series.

Frequently Asked Questions

Conclusion: Turn the Model into Marks

The credit risk PD LGD EAD model rewards candidates who can both state the formula and explain how each parameter flows into provisioning, capital, and RAROC. Move from theory to timed practice: attempt a full-length Risk Management mock test on iibf.store, target any gaps in the IRB or RAROC sections, and revise until the equation feels intuitive. Precise risk measurement in study mirrors the discipline banks apply in the field.