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Credential Stuffing Expected Loss Estimator

This estimator translates credential stuffing traffic into expected financial loss by modeling successful unauthorized logins, the share that produces harmful account abuse, and the average loss per abused account. It is useful for digital product, fraud, identity, and security teams evaluating the cost of automated login attacks.

The calculation keeps authentication success separate from downstream abuse. That helps distinguish controls that stop login attempts from controls that limit fraud after access. Use organization-specific telemetry where available, because attack mix, password reuse, customer behavior, and reimbursement practices can change the result substantially.

Enter your assumptions

attempts
%
%
USD
months
Result
—
Expected loss
Expected unauthorized logins—
Expected abused accounts—
Average monthly loss—

1. Enter attack volume

Use observed or forecast automated credential stuffing attempts per month.

2. Set successful login rate

Enter the percentage of attempts that result in unauthorized access.

3. Set harmful abuse rate

Estimate the share of unauthorized logins that lead to fraud, data access, loyalty theft, or another loss event.

4. Enter loss severity

Use the average direct and operational loss per abused account.

5. Choose the period

Set the number of months represented by the scenario.

6. Review expected loss

Compare expected unauthorized logins, abused accounts, monthly loss, and total loss.

Unauthorized logins = Attempts × Months × Login success rate Abused accounts = Unauthorized logins × Harmful abuse rate Expected loss = Abused accounts × Average loss per abused account

Percentage inputs are converted to decimals. The model assumes stable rates and treats each abused account as one loss event.

What the result means

Use the result as a scenario-based planning estimate. Compare several plausible inputs rather than relying on one point value.

This calculator does not replace a formal risk assessment, incident analysis, legal advice, or financial advice.

Given: 500,000 attempts per month, a 0.08% login success rate, 12% harmful abuse, $180 average loss, and 12 months.

Calculation: Unauthorized logins = 500,000 × 12 × 0.0008 = 4,800. Abused accounts = 4,800 × 0.12 = 576. Expected loss = 576 × $180 = $103,680.

Result: The modeled annual expected loss is $103,680.

Why are login success and abuse separate?

Not every unauthorized login creates a measurable loss. Separating the rates makes the attack path easier to calibrate and control.

Should blocked attempts be included?

Include attempts in the population used to calculate your observed success rate. Mixing a pre-control attempt count with a post-control success rate can distort the estimate.

Can one account be counted more than once?

This model treats abused accounts as loss events. If repeat abuse is common, use event-level data or adjust the average loss accordingly.

Does average loss include customer support?

It can. Include reimbursements, fraud operations, support, investigation, notification, and other costs relevant to your decision.

How is this different from risk exposure?

Expected loss estimates the average modeled loss. Risk exposure may also display gross and residual values after controls or use a broader range of consequences.