ISA 530 · Insurance

Sample Size Calculator for Insurance

Pre-configured sampling guidance for insurance audits. Covers claims file sampling, premium income testing, and IFRS 17 reserve component verification with ISA 530 methodology.

ISA 530 · LIVEv2026.04MUS

Sample size, defended.
Not just computed.

Session
0x4EFC
Fiscal Year
FY 2026
Confidence
95%
inputs.conf
methodology.conf
README.md
01// engagement— ISA 530.4
02entity_name=
03fiscal_year_end=
04currency=
05public_interest=
06first_year=
09// method— ISA 530.A4–A14
10sampling.method=
11method.rationale=
Method rationale · ISA 530.A4 documentation
14// parameters— ISA 530.7–8
15confidence_level=
16population=
17tolerable_misstmt=€ · TM ≤ PM
18expected_misstmt=€ · EM · optional
19confidence.rationale=
20tm.rationale=
21em.rationale=
Parameter rationales · ISA 530.9 documentation
24// finite_population_correction— optional
25population_item_count=items · FPC applied if >10%
30// objective— ISA 530.6 · what you're testing + what counts as error
31assertion_tested=
32misstatement_definition=
Objective · ISA 530.6 what you test + error definition
35// population_completeness— ISA 530.5(f) · population must be complete
36population.definition=
37completeness_test=
38sampling_unit=
Population · definition + completeness test
40// stratification— ISA 530.A8 · optional; reduces variability
No strata defined. Stratification is optional — add strata if population has very different sub-groups (by $ size, risk, or nature).
48stratification.rationale=
Stratification · ISA 530.A8 (optional)
50// selection_method— ISA 530.8 · how items are selected
51selection.method=
52selection.rationale=
Selection method · ISA 530.8
55// evaluation— ISA 530.12–14 · MLE / tainting
56
Evaluation · ISA 530.12–14 MLE / tainting
65// sensitivity— EM ±20% impact on n
Enter population and tolerable misstatement to see sensitivity.
Sensitivity · EM ±20% impact on n
70// method_comparison— MUS vs Classical
Enter inputs to compare methods.
Method comparison · MUS vs Classical
75// risk_flags— 15-rule engine · regulator deficiency patterns
Enter inputs to run risk analysis.
Risk flags · regulator deficiency intelligence
80// conclusion— ISA 530.15 · narrative evaluation
81conclusion.narrative=
82qualitative_factors=
Conclusion · ISA 530.15 narrative + qualitative
awaiting input·3/8 core fieldsEUR·MUS
previewwp-mus-2026.pdf
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Sample size
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Sampling interval
Population ÷ n
Top stratum
Items ≥ interval
Max errors
Before exceeding TM
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Full ISA 530 methodology with paragraph citations
Step-by-step sample size calculation workings
TM and EM sensitivity analysis tables
Method comparison (MUS vs Classical) with rationale
Risk intelligence flags with ISA references
Sample evaluation section with Stringer bound (MUS)
Population selection documentation and checklist
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Sampling Working Paper
ISA 530 (Revised) · ISA 500
1Sampling ParametersISA 530.7–9
2Sample Size CalculationISA 530.A10–A11
3Method RationaleISA 530.A4–A5
4Sensitivity AnalysisTM & EM ±
5Risk IntelligenceISA 530.A2–A8
6Selection MethodISA 530.A12–A14
7Evaluation CriteriaISA 530.14–15
8Documentation ChecklistISA 530.9
Prepared by ________Reviewed by ________Date ________
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Sampling Considerations for Insurance

Insurance audits deal with large, statistically significant populations of policies and claims. Premium income testing covers thousands of policies, while claims sampling must address both reported claims and the incurred-but-not-reported (IBNR) reserve. Under IFRS 17, the components of insurance contract liabilities — fulfilment cash flows, risk adjustment, and contractual service margin — add further sampling requirements.

Sampling focus: Insurance

Claims file sampling is the primary substantive test for insurance liabilities. MUS applied to the population of reported claims selects larger claims with higher probability, but the auditor should also ensure adequate coverage of smaller claims to test for systematic processing errors. Premium income sampling verifies that premiums are correctly recorded, classified, and earned over the policy period.

Key sampling considerations

Stratify the claims population by line of business (motor, property, liability, life) and by status (open, closed, reopened) — each stratum has different characteristics and reserve methodologies.

IBNR reserves cannot be tested by sampling individual items — instead, sample the actuarial model inputs (claim frequency, severity, development factors) to test the reasonableness of the aggregate estimate.

Premium income sampling should cover the full policy lifecycle — test new business, renewals, mid-term adjustments, and cancellations to verify correct recognition timing.

Reinsurance recoveries should be sampled separately to verify that ceded amounts are correctly calculated and that the reinsurer's creditworthiness supports recoverability.

Under IFRS 17, sample contracts across measurement models (general, variable fee, PAA) to test the correct classification and initial recognition of insurance contract groups.

Frequently asked questions

What are the key sampling considerations for insurance audits?
Stratify the claims population by line of business (motor, property, liability, life) and by status (open, closed, reopened) — each stratum has different characteristics and reserve methodologies. IBNR reserves cannot be tested by sampling individual items — instead, sample the actuarial model inputs (claim frequency, severity, development factors) to test the reasonableness of the aggregate estimate. Premium income sampling should cover the full policy lifecycle — test new business, renewals, mid-term adjustments, and cancellations to verify correct recognition timing. Reinsurance recoveries should be sampled separately to verify that ceded amounts are correctly calculated and that the reinsurer's creditworthiness supports recoverability. Under IFRS 17, sample contracts across measurement models (general, variable fee, PAA) to test the correct classification and initial recognition of insurance contract groups.
What is the sampling focus for insurance?
Claims file sampling is the primary substantive test for insurance liabilities. MUS applied to the population of reported claims selects larger claims with higher probability, but the auditor should also ensure adequate coverage of smaller claims to test for systematic processing errors. Premium income sampling verifies that premiums are correctly recorded, classified, and earned over the policy period.
How does the ISA 530 MUS formula work?
The standard MUS formula is: n = (Population x Confidence Factor) / (Tolerable Misstatement - Expected Misstatement x Expansion Factor). The confidence factor reflects the acceptable risk of incorrect acceptance. Expected misstatement increases the required sample size because the auditor must leave headroom above the expected level before reaching the tolerable threshold.

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