AI Modules/Procedures/Sampling

SA 530 · Procedure· विधि

Sampling

The formula shown. Selections seeded. Peer reviewers obtain identical వౌచర్‌లు.

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CORAA Sampling, per-WP plans with SA 530 formula rendered

When a peer reviewer asks why the sample size is 62, 'the engine picked them' is not an answer. CORAA renders the SA 530 formula explicitly, sample size equals ceiling of (confidence factor × population value) divided by performance materiality. విశ్వాసం factors derive from the Poisson distribution. Selections are seeded, peer reviewers running the same seed obtain identical వౌచర్‌లు.

Per-Working-Paper plans, not one engagement-wide sample
Formula rendered on every plan: ceil((CF × Population value) / PM)
విశ్వాసం factors: 3.0 at 95% (high risk), 2.3 at 90% (medium), 1.6 at 80% (low)
Force-census rows for 100% testing, year-end manual JEs, related parties, employee benefits
Seeded selection per SA 230 Para. 8, peer reviewers obtain identical వౌచర్‌లు
Lock requires the rationale captured at sign-off

Two paths, one ledger

The old way, and ours.

Two paths to the same audit conclusion. One leaves traces; the other doesn't.

Traditional

The old way

-Sample sizes set on judgment, 'around 30 వౌచర్‌లు per balance head'
-విశ్వాసం factor and risk assessment rarely documented
-Selection done by picking 'every nth' వౌచర్ or visually significant ones
-Peer reviewer cannot retrace the selection logic
-Adjustments mid-engagement (more వౌచర్‌లు added) not documented

Defensible only on the ఆడిటర్'s word. SA 230 reproducibility test rarely satisfied.

CORAA

On the Ledger

Sample size derived from a formula visible on the Working Paper
విశ్వాసం factor explicit, 3.0/2.3/1.6 mapped to risk level
MUS for high-value populations; simple random for low-value, homogeneous
Force-census rows configurable: year-end manual JEs, RPT, employee benefits
Selection seeded, engagement ID + balance head code derives the seed
Peer reviewer with the same seed obtains identical వౌచర్‌లు

Every sample defensible. SA 230 reproducibility satisfied. NFRA-inspection ready.

How it works

Three steps. Every trace logged.

Step 01

Set the risk level per balance head

For each Working Paper, the ఆడిటర్ sets the Risk of Material Misstatement, High (95% confidence), Medium (90%), or Low (80%). The confidence factor follows: 3.0 / 2.3 / 1.6 from the Poisson distribution at -ln(α).

Step 02

Sample size renders

Sample size = ceil((confidence factor × population value) / Performance Materiality). Worked example: Trade Payables population ₹5 crore, PM ₹35.8 lakh, high risk = ceil((3.0 × 5,00,00,000) / 35,80,000) = 42 వౌచర్‌లు. The formula is rendered on the Working Paper.

Step 03

Lock the plan

Locking generates the deterministic వౌచర్ selection per SA 230 Para. 8. The seed is derived from engagement ID + balance head code. A peer reviewer running the same seed obtains the same వౌచర్‌లు. Locked plans are read-only; unlock requires a documented note.

Inside the module

What you actually get.

Formula visible, no black box

Every Working Paper renders the sample-size formula explicitly: ceil((విశ్వాసం factor × Population value) / Performance Materiality). The confidence factor, the population value, and the PM all show on the page. The ఆడిటర్ can defend the sample size on the spot.

›Formula shown on every plan
›విశ్వాసం factor explicit with derivation
›ఆడిటర్'s adjustment (with reason) supported
›Coverage percentage shown alongside count

విశ్వాసం factors from the Poisson distribution

ICAI SA 530 Para A11 + Appendix 3 derives confidence factors from -ln(α) where α is the acceptable sampling risk. High risk → 95% confidence → CF 3.0. Medium → 90% → CF 2.3. Low → 80% → CF 1.6. CORAA renders the math.

›High risk: 95% confidence, CF = 3.0
›Medium risk: 90% confidence, CF = 2.3
›Low risk: 80% confidence, CF = 1.6
›ICAI Appendix 3 table referenced

Force-census rows for 100% testing

Some rows demand 100% testing regardless of sample math, year-end manual journals (last 14 days), related-party transactions, employee benefits over PM threshold. Configure once ఎంగేజ్‌మెంట్‌కు; CORAA enforces.

›Year-end manual JEs (last 14 days)
›Related-party transactions (when RPT list is set)
›Employee benefits above PM
›Other high-risk patterns configurable

Seeded reproducibility per SA 230

Selection is seeded from a deterministic value: engagement ID + balance head code, hashed via SHA-256. A peer reviewer running the same seed obtains the same వౌచర్‌లు. SHA-256 algorithm and numpy version are pinned, so identical seeds yield identical selections years later.

›Deterministic seed derivation
›SHA-256 and numpy version pinned
›Peer reviewer can retrace
›ఆడిట్ trail preserves seed value

Frequently asked

Answers, up front.

Per ICAI SA 530 Para A11 and Appendix 3: sample size equals ceiling of (విశ్వాసం factor × Population value) divided by Performance Materiality. The confidence factor depends on risk level: 3.0 for high risk (95% confidence), 2.3 for medium (90%), 1.6 for low (80%). Worked example: Trade Payables population ₹5 crore, PM ₹35.8 lakh, high risk = ceil((3.0 × 5,00,00,000) / 35,80,000) = 42 వౌచర్‌లు.

Each random selection is seeded with a deterministic value derived from the engagement ID and balance head code, hashed via SHA-256 and pinned to a specific numpy version. A peer reviewer running the same seed obtains the same వౌచర్‌లు. The seed is rendered explicitly on every Working Paper. This satisfies SA 230 Para. 8 reproducibility.

MUS (Monetary Unit Sampling) is used for high-value populations where the top items concentrate most of the monetary value, for example, Trade Payables where 80% of value sits in 20% of వౌచర్‌లు. MUS picks వౌచర్‌లు in proportion to their amount, achieving high coverage with fewer items. Simple random is used for low-value, homogeneous populations. CORAA picks the method automatically based on the population's distribution; the choice is rendered on the Working Paper.

అవును, the ఆడిటర్ can adjust the sample size up or down. Every adjustment requires a reason captured in the ఆడిట్ ట్రెయిల్ per SA 230. Typical adjustments: +N for prior-year ఫలితాలు, -N for highly automated processes, +N for sub-category coverage. The adjusted number, the reason, and the ఆడిటర్ name are all logged.

See it on a real ledger

Run sampling on one of your engagements.

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