How to use this calculator
- Define the complete eligible population.
- Choose confidence and enter the expected exception rate.
- Set the precision needed for the audit decision.
- Add an unusable-record allowance and review valid sample versus total draw.
#2008 · Legal & Compliance Tool
KYC Verification Audit Sample Size Calculator
Estimate a statistically based sample size for an audit of KYC verification records using population size, confidence level, expected exception rate, and desired margin of error. The result includes a finite-population adjustment and an allowance for unusable selections. It supports audit planning, but professional judgment, risk stratification, and applicable audit methodology may require a larger or targeted sample.
Calculator
Statistical sampling assumptionsFormula
Initial n₀ = z² × p × (1 − p) ÷ e²
Finite population n = n₀ ÷ [1 + (n₀ − 1) ÷ N]
Records to draw = ceiling[n ÷ (1 − unusable rate)]
Finite population n = n₀ ÷ [1 + (n₀ − 1) ÷ N]
Records to draw = ceiling[n ÷ (1 − unusable rate)]
N is population size, p is expected exception proportion, e is margin of error, and z is the selected confidence coefficient.
What the result means
The valid sample is the estimated number of usable KYC verification records needed to estimate an exception proportion under the entered statistical assumptions. The draw count adds an allowance for unusable selections.
Random sampling supports statistical inference. Targeted, judgmental, discovery, or control-testing samples serve different purposes and should not be represented by this result.
Example calculation
For 5,000 records, 95% confidence, a 5% expected exception rate, and a 3% margin of error, the finite-population estimate is 195 valid records. A 5% unusable allowance increases the draw to 206 records.
Tips for better results
- Use a defensible random-selection method for statistical inference.
- Separate materially different risk strata before sampling.
- Use prior evidence to estimate the exception rate when available.
- Document exclusions and unusable selections.
- Increase scope when qualitative risk or methodology requires it.
Frequently asked questions
Why does expected exception rate affect the KYC verification audit sample?
The estimated variance of an exception proportion changes with the expected rate, which changes the required statistical sample.
When should I use 99% confidence instead of 95%?
Use the level required by the audit objective or methodology; higher confidence generally increases the sample.
Does a larger population always require a much larger sample?
Not after the population becomes large relative to the sample because the finite-population adjustment has less effect.
Can this result be used for targeted high-risk cases?
No. The formula assumes a probability-style sample for estimating a proportion; targeted testing needs a separate rationale.
Why is the records-to-draw result larger than the valid sample?
It compensates for the entered share of records expected to be unavailable, duplicate, or otherwise unusable.
Related calculators
Sampling model variables
| Variable | Meaning |
|---|---|
| N | Eligible population size |
| z | Confidence coefficient |
| p | Expected exception proportion |
| e | Margin of error |
| Unusable rate | Allowance added to selections |