AI Modules/Procedures/மாதிரி எடுப்பு

SA 530 · Procedure· विधि

மாதிரி எடுப்பு

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

Start the free trial →Book a walk-through

CORAA மாதிரி எடுப்பு, 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. Confidence factors derive from the Poisson distribution. Selections are seeded, peer reviewers running the same seed obtain identical vouchers.

Per-Working-Paper plans, not one எங்கேஜ்மென்ட்-wide sample
Formula rendered on every plan: ceil((CF × Population value) / PM)
Confidence 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 vouchers
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 vouchers per balance head'
-Confidence factor and ஆபத்து மதிப்பீடு rarely documented
-Selection done by picking 'every nth' voucher or visually significant ones
-Peer reviewer cannot retrace the selection logic
-Adjustments mid-எங்கேஜ்மென்ட் (more vouchers added) not documented

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

CORAA

On the Ledger

Sample size derived from a formula visible on the Working Paper
Confidence 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, எங்கேஜ்மென்ட் ID + balance head code derives the seed
Peer reviewer with the same seed obtains identical vouchers

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 கணக்காய்வாளர் 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 vouchers. The formula is rendered on the Working Paper.

Step 03

Lock the plan

Locking generates the deterministic voucher selection per SA 230 Para. 8. The seed is derived from எங்கேஜ்மென்ட் ID + balance head code. A peer reviewer running the same seed obtains the same vouchers. 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((Confidence factor × Population value) / Performance Materiality). The confidence factor, the population value, and the PM all show on the page. The கணக்காய்வாளர் can defend the sample size on the spot.

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

Confidence factors from the Poisson distribution

ICAI SA 530 Para A11 + Appendix 3 derives confidence factors from -ln(α) where α is the acceptable மாதிரி எடுப்பு 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 per எங்கேஜ்மென்ட்; 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: எங்கேஜ்மென்ட் ID + balance head code, hashed via SHA-256. A peer reviewer running the same seed obtains the same vouchers. 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
›கணக்காய்வுத் தடம் preserves seed value

Frequently asked

Answers, up front.

Per ICAI SA 530 Para A11 and Appendix 3: sample size equals ceiling of (Confidence 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 vouchers.

Each random selection is seeded with a deterministic value derived from the எங்கேஜ்மென்ட் 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 vouchers. The seed is rendered explicitly on every Working Paper. This satisfies SA 230 Para. 8 reproducibility.

MUS (Monetary Unit மாதிரி எடுப்பு) 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 vouchers. MUS picks vouchers in proportion to their amount, achieving high coverage with fewer items. Simple random is used for low-value, homogeneous populations. CORAA picks the method தானாகவே based on the population's distribution; the choice is rendered on the Working Paper.

Yes, the கணக்காய்வாளர் can adjust the sample size up or down. Every adjustment requires a reason captured in the கணக்காய்வுத் தடம் per SA 230. Typical adjustments: +N for prior-year findings, -N for highly தானியக்க processes, +N for sub-category coverage. The adjusted number, the reason, and the கணக்காய்வாளர் name are all logged.

See it on a real ledger

Run மாதிரி எடுப்பு on one of your engagements.

Bring a Trial Balance and a General Ledger. We'll walk through procedures end-to-end on your data, not a sandbox.

Start the free trial →Book a walk-through