AI Modules/Procedures/सैम्पलिंग

SA 530 · प्रोcedure· विधि

सैम्पलिंग

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

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

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

Per-Working-Paper plans, not one एंगेजमेंट-wide sample
Formula rendered on every plan: ceil((CF × Population value) / PM)
Confidence factors: 3.0 at 95% (high जोखिम), 2.3 at 90% (medium), 1.6 at 80% (low)
Force-census rows for 100% testing, वर्ष-end manual JEs, related parties, employee benefits
Seeded selection per SA 230 Para. 8, peer reviewers obtain identical वाउचर
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 वाउचर per balance head'
-Confidence factor and जोखिम मूल्यांकन rarely documented
-Selection done by picking 'every nth' वाउचर or visually significant ones
-Peer reviewer cannot retrace the selection logic
-Adjustments mid-एंगेजमेंट (अधिक वाउचर added) not documented

Defensible only on the ऑडिटर'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 जोखिम level
MUS for high-value populations; simple random for low-value, homogeneous
Force-census rows configurable: वर्ष-end manual JEs, RPT, employee benefits
Selection seeded, एंगेजमेंट ID + balance head code derives the seed
Peer reviewer with the same seed obtains identical वाउचर

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

How it works

Three steps. Every trace logged.

Step 01

Set the जोखिम level per balance head

For each Working Paper, the ऑडिटर sets the जोखिम 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 मटीरियलिटी). Worked example: Trade Payables population ₹5 crore, PM ₹35.8 lakh, high जोखिम = ceil((3.0 × 5,00,00,000) / 35,80,000) = 42 वाउचर. The formula is rendered on the Working Paper.

Step 03

Lock the plan

Locking generates the deterministic वाउचर selection per SA 230 Para. 8. The seed is derived from एंगेजमेंट ID + balance head code. A peer reviewer running the same seed obtains the same वाउचर. Locked plans are पढ़ें-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 मटीरियलिटी). The confidence factor, the population value, and the PM सभी show on the पृष्ठ. The ऑडिटर can defend the sample size on the spot.

›Formula shown on every plan
›Confidence factor explicit with derivation
›ऑडिटर'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 सैम्पलिंग जोखिम. High जोखिम → 95% confidence → CF 3.0. Medium → 90% → CF 2.3. Low → 80% → CF 1.6. CORAA renders the math.

›High जोखिम: 95% confidence, CF = 3.0
›Medium जोखिम: 90% confidence, CF = 2.3
›Low जोखिम: 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, वर्ष-end manual journals (last 14 दिन), related-party transactions, employee benefits over PM threshold. Configure once per एंगेजमेंट; CORAA enforces.

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

Seeded reproducibility per SA 230

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

›Deterministic seed derivation
›SHA-256 and numpy version pinned
›Peer reviewer can retrace
›ऑडिट trail 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 मटीरियलिटी. The confidence factor depends on जोखिम level: 3.0 for high जोखिम (95% confidence), 2.3 for medium (90%), 1.6 for low (80%). Worked example: Trade Payables population ₹5 crore, PM ₹35.8 lakh, high जोखिम = ceil((3.0 × 5,00,00,000) / 35,80,000) = 42 वाउचर.

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

MUS (Monetary Unit सैम्पलिंग) 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 वाउचर. MUS picks वाउचर 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.

हाँ, the ऑडिटर can adjust the sample size up or down. Every adjustment requires a reason captured in the ऑडिट ट्रेल per SA 230. Typical adjustments: +N for prior-वर्ष निष्कर्ष, -N for highly automated processes, +N for sub-category coverage. The adjusted number, the reason, and the ऑडिटर name are सभी logged.

See it on a real ledger

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