Exposure at Default and Credit Conversion Factors

A company with a $100 million revolving credit line might only have $20 million drawn today. A bank measuring its risk on that facility can't just use the $20 million currently outstanding, because struggling borrowers tend to draw down the rest of their available credit right before they default — often precisely because they're running out of cash from other sources. Exposure at default (EAD) is the estimate of what the bank is actually exposed to at the moment of default, not what's drawn today.

The formula

$$EAD = \text{Drawn Amount} + CCF \times \text{Undrawn Commitment}$$

In words: take what's already outstanding, and add a fraction — the CCF, usually estimated from a historical dataset of defaulted borrowers' drawdown patterns in the year before default — of whatever credit is still available but unused. A CCF of 100% assumes borrowers draw down everything available before defaulting; a CCF of 0% assumes the undrawn portion adds no risk at all. Real historical CCFs for revolving corporate facilities typically sit somewhere in the 30–75% range depending on facility type and borrower quality.

Worked example

A company has a $100 million revolving credit facility, currently drawn to $20 million, leaving $80 million undrawn. The bank's historical data on similar facilities implies a CCF of 50% for borrowers approaching distress.

1. Modeled draw of the undrawn portion. $80 \times 0.50 = 40$, i.e. $40m.
2. Exposure at default. $20 + 40 = 60$, i.e. $60m.

The bank's regulatory capital and internal risk pricing for this facility are based on a $60 million exposure, not the $20 million currently drawn — three times the naive current-balance figure. If this same borrower were instead assessed at a lower-quality CCF of 75% (reflecting a riskier facility type more prone to last-minute maximum drawdown), exposure at default would rise to $20 + 0.75 \times 80 = 80$, i.e. $80m, materially changing the capital the bank must hold and the loss it should expect if the borrower defaults.

What this means in practice

EAD, together with probability of default and loss given default, is one of the three core inputs to regulatory capital and internal expected-loss calculations under frameworks like Basel's internal-ratings-based approach — banks must estimate and validate all three, and EAD is often the most overlooked of the three by newcomers, since it requires modeling borrower behavior, not just current balance sheet facts.