MCom Auditing & Assurance

Faculty of Commerce, Law and Management · CDU-BUS-AUD-900 · MM · v1.0.0

The calculations an audit file must show: overall and performance materiality and the clearly-trivial threshold (ISA 320, ISA 450), the attribute sample size for a test of controls and the monetary-unit sample for a test of details (ISA 530), and the Altman Z-score as a going-concern indicator (ISA 570).

Lineage

Conquest Designer University, 2026 — authored from the brief and the IAASB/IFAC standards; no prior institution is claimed

Final research project

How much evidence is enough: materiality, sample size and the going-concern signal on one audit file

Syllabus

Materiality: benchmark, percentage, performance materiality, clearly trivial (ISA 320 ¶10–11, ISA 450 ¶A2)
Tests of controls: attribute sampling, tolerable and expected deviations, confidence (ISA 530)
Tests of details: monetary-unit sampling, reliability factor, sampling interval, basic precision (ISA 530)
Going concern: events and conditions, and a quantitative distress indicator (ISA 570 (Revised) ¶A3; Altman 1968)

Reference

Overall materiality: benchmark × a percentage set by professional judgement (ISA 320 ¶10, ¶A3–A7)
Performance materiality: an amount below overall materiality (ISA 320 ¶11)
Attribute sample size: smallest n with P(deviations ≤ k | n, tolerable rate) ≤ 1 − confidence (binomial)
MUS sample size: book value × R ÷ tolerable misstatement, R = −ln(1 − confidence) (Poisson, no expected misstatement)
Altman Z: 1.2·WC/TA + 1.4·RE/TA + 3.3·EBIT/TA + 0.6·MVE/TL + 1.0·Sales/TA; < 1.81 distress, > 2.99 safe

Units

Materiality, performance materiality and clearly trivial · Attribute sample size for a test of controls · Monetary-unit sample: size, interval and basic precision · Going-concern indicator (Altman Z)

Role when narrated: You are an audit engagement partner explaining a judgement on the file to the audit committee. Name the standard, show the calculation, and say what the number obliges the team to do next.

Materiality, performance materiality and clearly trivial · materiality

Try with the example

Given the benchmark and the percentages judged appropriate, what are overall materiality, performance materiality and the clearly-trivial threshold?

Reference: ISA 320 ¶10 (overall materiality), ¶11 (performance materiality), ¶A3–A7 (benchmarks and percentages); ISA 450 ¶A2 (clearly trivial)

Returns

overallMateriality: Benchmark × percentage
performanceMateriality: Overall × performance factor
clearlyTrivialThreshold: Overall × trivial factor

The example

{
  "benchmark": "profitBeforeTax",
  "benchmarkAmount": 2400000,
  "percentage": 0.05,
  "performanceFactor": 0.75,
  "trivialFactor": 0.05
}

Agents: POST /faculty/mcom-auditing--assurance/materiality with JSON {pan, …inputs} and the x-402-payment header. Manifest.

Attribute sample size for a test of controls · attribute_sample_size

Example filled in — consult below

How many items must be tested so that, finding no more than the planned number of deviations, the control can be relied on at this confidence?

Reference: ISA 530 Audit Sampling ¶7, ¶A10–A13 (tolerable rate, sampling risk); sample size by the binomial distribution as in the AICPA Audit Sampling guide

Returns

sampleSize: Smallest n meeting the risk
riskOfOverreliance: P(deviations ≤ planned | n, tolerable rate)

The example

{
  "confidence": 0.95,
  "tolerableRate": 0.05,
  "plannedDeviations": 1
}

Agents: POST /faculty/mcom-auditing--assurance/attribute_sample_size with JSON {pan, …inputs} and the x-402-payment header. Manifest.

Monetary-unit sample: size, interval and basic precision · mus_planning

Try with the example

For a test of details on this balance, how many monetary units are sampled, at what interval, and what is the basic precision if none is misstated?

Reference: ISA 530 Audit Sampling ¶A11 (tolerable misstatement); monetary-unit sampling with the Poisson reliability factor R = −ln(1 − confidence), no expected misstatement

Returns

reliabilityFactor: R = −ln(1 − confidence)
sampleSize: ceil(book value × R ÷ tolerable)
samplingInterval: Book value ÷ sample size
basicPrecision: R × interval: the upper limit when no misstatement is found

The example

{
  "bookValue": 5000000,
  "tolerableMisstatement": 150000,
  "confidence": 0.95
}

Agents: POST /faculty/mcom-auditing--assurance/mus_planning with JSON {pan, …inputs} and the x-402-payment header. Manifest.

Going-concern indicator (Altman Z) · going_concern_zscore

Try with the example

Does the Altman Z-score put the entity in the distress, grey or safe zone — one quantitative input to the going-concern assessment?

Reference: Altman, E. I. (1968), Journal of Finance 23(4) 589–609 — the original five-ratio Z for public manufacturers; ISA 570 (Revised) ¶A3 (events and conditions that may cast doubt)

Returns

z: The Z-score
zone: distress (< 1.81), grey (1.81–2.99) or safe (> 2.99)
goingConcernFlag: True outside the safe zone: the indicator calls for further procedures

The example

{
  "workingCapital": 500000,
  "retainedEarnings": 800000,
  "ebit": 300000,
  "marketValueEquity": 1500000,
  "sales": 4000000,
  "totalAssets": 3000000,
  "totalLiabilities": 1200000
}

Agents: POST /faculty/mcom-auditing--assurance/going_concern_zscore with JSON {pan, …inputs} and the x-402-payment header. Manifest.