Expected Loss

Expected loss is one of the most important concepts in credit risk — the way banks and lenders estimate, on average, how much they're likely to lose from loans going bad. It underpins loan provisioning, pricing and capital. This guide explains what expected loss is, the formula and its three components, how it differs from unexpected loss, and where it's used — in clear, plain language. It's relevant to anyone studying risk management, banking or finance.

What is expected loss?

Expected loss (EL) is the average loss a lender expects to suffer on a credit exposure over a given period, taking into account the chance that the borrower defaults and how much would be lost if they did. It's not a prediction that a specific loan will default; rather, it's a statistical expected value — the loss you'd expect on average across many similar exposures. Because lending always carries some risk of default, expected loss is treated as a foreseeable cost of doing business, to be provided for and priced in.

The formula and its three components

Expected loss is calculated as the product of three components:

EL = PD × LGD × EAD

• PD — Probability of Default — the likelihood that the borrower defaults over the period (for example, a 2% chance over one year).
• LGD — Loss Given Default — the proportion of the exposure that would actually be lost if default occurs, after any recoveries (so LGD = 1 minus the recovery rate). For example, an LGD of 40% means 40% of the exposure is lost and 60% recovered.
• EAD — Exposure at Default — the amount the lender is exposed to at the time of default (broadly, the outstanding balance plus any further drawdowns expected).

Multiplying the three gives the expected loss. For instance, a £1,000,000 exposure (EAD) with a 2% PD and 40% LGD has an expected loss of 0.02 × 0.40 × £1,000,000 = £8,000.

Estimating the components

Each component is estimated in its own way, and getting them right is where much of the work in credit risk lies. PD is typically estimated from credit scoring or rating models, historical default data, and borrower characteristics — a stronger borrower has a lower PD. LGD depends heavily on collateral and seniority: a well-secured loan (say, a mortgage backed by property) has a low LGD because much can be recovered, whereas an unsecured loan has a high LGD. EAD is straightforward for a simple term loan (the outstanding balance) but harder for products like credit lines, where the borrower might draw down more before defaulting. Because expected loss is the product of all three, improving the estimate of any one improves the whole — and errors in any one flow straight through to the result.

Expected vs unexpected loss

It's important to distinguish expected loss from unexpected loss. Expected loss is the average — it's anticipated, and is covered by provisions (and priced into lending margins). Unexpected loss is the variability around that average — the risk that losses in a bad year turn out far higher than expected. Because unexpected losses can't be predicted in the same way, they're covered not by provisions but by capital, which acts as a buffer against worse-than-expected outcomes. This split — provisions for expected loss, capital for unexpected loss — is fundamental to how banks manage credit risk.

Where expected loss is used

Expected loss is central to several areas. It drives loan-loss provisioning, including under the IFRS 9 expected-credit-loss model, which requires lenders to provide for expected losses on financial assets. It feeds into loan pricing, so that the interest charged covers the expected cost of defaults. It's part of the Basel regulatory framework for banks. And it supports credit risk management generally — assessing and comparing the riskiness of different exposures and portfolios. Understanding the PD, LGD and EAD components is fundamental to all of these.

Frequently asked questions

What is expected loss?

The average loss a lender expects on a credit exposure over a period, reflecting the chance of default and the loss if it occurs — a statistical expected value, not a prediction for a single loan.

What is the expected loss formula?

EL = PD × LGD × EAD: the probability of default, multiplied by the loss given default (proportion lost), multiplied by the exposure at default.

What's the difference between expected and unexpected loss?

Expected loss is the anticipated average, covered by provisions and pricing. Unexpected loss is the variability around it — the risk of worse-than-expected outcomes — covered by capital.

Where is expected loss used?

In loan-loss provisioning (including IFRS 9 expected credit losses), loan pricing, the Basel framework for banks, and credit risk management generally.

Build risk skills with Learnsignal

Credit-risk concepts like expected loss are part of modern finance. Learnsignal's tutor-led ACCA and CIMA courses build the financial and risk foundations behind them — with flexible, supported online study that fits around work.