Expected loss combines probability of default, exposure at default, and loss given default to estimate average credit loss over a defined horizon.
Expected loss (EL) is the average credit loss predicted for an exposure or portfolio over a defined horizon. In a common credit-risk model, it combines the probability that default occurs, the amount expected to be exposed at default, and the percentage of that exposure expected to be lost after recoveries.
Expected loss is a model average, not the amount that a single loan is guaranteed to lose. It is also not automatically the same as an accounting allowance under U.S. CECL or IFRS 9.
PD x EAD x LGD, with PD and LGD expressed as decimals and EAD in currency.For one exposure under a simplified model:
| Component | Meaning | Unit | Key definition choice |
|---|---|---|---|
| Probability of default (PD) | Likelihood that the obligor defaults during the horizon | Decimal or percentage | Default trigger and horizon |
| Exposure at default (EAD) | Amount expected to be exposed when default occurs | Currency | Drawdowns, repayments, interest, and conversion factors |
| Loss given default (LGD) | Economic loss as a share of EAD if default occurs | Decimal or percentage | Recoveries, costs, timing, collateral, and seniority |
The calculation is only as coherent as its components. A one-year PD should not be combined casually with an LGD from a different default definition or an EAD measured on a different exposure basis.
Assume a lender estimates for a one-year horizon:
2%$1,000,00045%The expected loss is:
The $9,000 is the exposure’s average modeled loss contribution. The actual one-year outcome may be no default and no credit loss, or default with a loss materially above or below $9,000.
The 45% LGD can correspond to a 55% net economic recovery rate when both use the same EAD, recovery cash flows are discounted to the default date, and material workout costs are deducted.
Suppose a portfolio contains three segments:
| Segment | PD | EAD | LGD | Expected loss |
|---|---|---|---|---|
| Secured commercial | 1.0% | $40 million | 25% | $100,000 |
| Unsecured consumer | 4.0% | $10 million | 70% | $280,000 |
| Small business lines | 2.5% | $20 million | 45% | $225,000 |
| Total | $70 million | $605,000 |
The portfolio EL rate is about 0.86% of EAD, but the segment results show why the average should not be applied indiscriminately. Risk differs by default likelihood, utilization, collateral, seniority, and recovery process.
| Measure | Primary purpose | Typical horizon and mechanics |
|---|---|---|
| Economic expected loss | Pricing, underwriting, portfolio risk, and performance measurement | Defined by the model; often represented by PD x EAD x LGD |
| Basel regulatory EL | Prudential capital and provision comparison under specified rules | Regulatory PD, LGD, EAD, asset-class, and default requirements |
| U.S. CECL allowance | Financial reporting under ASC Topic 326 | Expected credit losses over the applicable contractual term for covered exposures |
| IFRS 9 ECL | Financial reporting under IFRS 9 | 12-month or lifetime ECL depending on impairment stage and other requirements |
| Realized credit loss | Measurement of losses that have occurred | Charge-offs, workout outcomes, or another defined realized-loss measure |
An institution can use PD/LGD methods within an accounting process, but that does not make a one-year regulatory or pricing EL equal to the financial-statement allowance. Contractual term, prepayments, discounting, staging, forecast periods, scenario weights, and scope can differ.
Expected loss is the center of the modeled loss distribution. Unexpected loss concerns the possibility that actual losses exceed that average. Pricing and allowances may address expected loss, while capital, limits, stress tests, and diversification analysis address severe but plausible deviations.
A loan spread equal to EL is not necessarily adequate compensation. Funding cost, operating expense, liquidity, capital usage, taxes, concentration, model uncertainty, and required return also matter.
2 instead of 0.02.EL is sensitive to sparse default data, changing underwriting, economic cycles, model selection, recovery lags, collateral valuation, and parameter correlation. Average estimates can conceal tail risk and concentration. Model outputs can also create false precision when default observations are limited or definitions are inconsistent.
This page is educational and is not accounting, regulatory, lending, investment, model-validation, or personalized financial advice.