Enter Control Inputs
Use the responsive form below. It displays three columns on large screens, two on medium screens, and one on mobile.
Example Data Table
| Scenario | Opportunities | Failures | Blended failure rate | At least one failure | Expected loss |
|---|---|---|---|---|---|
| Invoice approval | 240 | 6 | 5.40% | 73.64% | $4,539.57 |
| Access recertification | 180 | 2 | 3.22% | 44.54% | $1,739.87 |
| Trade surveillance | 520 | 4 | 1.42% | 24.89% | $710.43 |
Formula Used
Effectiveness = 0.35 × Design + 0.30 × Operating + 0.20 × Automation + 0.15 × Monitoring
Modeled failure rate = (1 − Effectiveness) × Change factor × Dependency factor
αprior = 1 + Prior weight × Modeled failure rate
βprior = 1 + Prior weight × (1 − Modeled failure rate)
Posterior mean = (αprior + Observed failures) / (αprior + βprior + Opportunities)
Probability of at least one failure = 1 − (1 − Posterior mean)Forecast events
Expected failures = Forecast events × Posterior mean
Expected loss = Expected failures × Loss per failure
How to Use This Calculator
- Enter the control name to label the analysis.
- Add the number of observed opportunities and failures.
- Score design, operation, automation, and monitoring effectiveness as percentages.
- Adjust change and dependency factors to reflect complexity and reliance.
- Set prior weight to control how strongly the modeled estimate influences the blended rate.
- Enter forecast exposure events and expected loss per failure.
- Select a confidence level for the empirical upper bound.
- Press the calculate button to show the result above the form.
- Use the CSV or PDF buttons to export the result summary.
FAQs
1. What does this calculator estimate?
It estimates how likely a control is to fail, blends model assumptions with observed evidence, and projects future exposure and loss.
2. Why are both modeled and empirical rates shown?
The modeled rate reflects design assumptions. The empirical rate reflects observed history. Seeing both helps compare expectation against actual control behavior.
3. What is prior weight?
Prior weight acts like equivalent historical observations. Higher values make the blended result lean more toward modeled assumptions than observed sample outcomes.
4. Why use change and dependency factors?
Controls often weaken during process changes or when they depend on upstream teams, systems, or data quality. These factors scale that added fragility.
5. What does the at least one failure result mean?
It estimates the chance that one or more failures occur across the forecast exposure events, not the chance of failure in a single execution.
6. Why is there an upper confidence bound?
The upper bound offers a more conservative view using observed data uncertainty. It helps risk teams plan for worse but plausible outcomes.
7. Can this calculator replace audit testing?
No. It supports judgment and prioritization. Audit testing, walkthroughs, sampling, and control design reviews are still required for assurance.
8. How should I interpret the risk band?
The band is a quick indicator based on the blended failure rate. Use it for screening, then review loss size and confidence results together.