Risk Management
Risk Quantification Monte Carlo
Simulated aggregate exposure distribution showing expected, P90 and tail-loss values across the register.
Why Quantify Aggregate Exposure
A risk register that only colours risks red, amber and green tells the board nothing about how much rand exposure it actually carries in total. Monte Carlo simulation converts the register into a distribution of possible aggregate losses, giving accounting officers and audit committees the expected and tail values they need to set reserves and appetite under King IV and sound COSO practice. AuditPro Core runs the simulation across your live register and surfaces expected, P90 and tail-loss figures.
The Numbers
AuditPro Core renders this view from your tenant's live, tamper-evident records. The figures below are illustrative sample data.
Expected annual loss
R 58 m
P90 exposure
R 141 m
planning value
Tail (P99)
R 287 m
worst 1%
Simulations
50 000
Simulated aggregate loss distribution
Contribution to expected loss by risk
| Risk | Expected (R m) | Share % |
|---|---|---|
| Revenue under-collection | 18.2 | 31 |
| Irregular expenditure | 12.6 | 22 |
| Infrastructure failure | 9.4 | 16 |
| Litigation exposure | 7.1 | 12 |
Figures shown are illustrative sample data for demonstration. AuditPro Core renders these views from your own tenant's live, tamper-evident records.
From Heat Maps to Distributions
Heat maps rank risks but cannot be added together meaningfully. A Monte Carlo model draws thousands of samples from each risk's likelihood and impact ranges to produce a single distribution of total possible loss.
Expected, P90 and Tail Loss
The expected value is the average outcome you would budget for; the P90 is the level exceeded only one year in ten; the tail loss is the average of the worst outcomes beyond that point. Reserves should be sized against the tail, not the average.
Correlation and Dependency
Risks rarely move independently — a fiscal shock can trigger several at once. Modelling correlation prevents the dangerous understatement that comes from assuming every risk is unrelated.
Garbage In, Garbage Out
A simulation is only as credible as the likelihood and impact ranges fed into it. Disciplined, evidence-based estimates and documented assumptions are what separate a decision tool from false precision.
How AuditPro Core Bridges the Gap
- Register-driven modelling: the simulation runs directly off your live risk register so quantified exposure stays in step with the qualitative assessment.
- Tail-loss visibility: expected, P90 and tail values are surfaced together so reserve and appetite decisions are anchored to the right point on the curve.
- Assumption traceability: every likelihood and impact range links back to its supporting evidence for audit committee challenge.
- Audit-ready export: produce the quantification schedule and assumption log for working papers and reserve-adequacy reviews.
Key Takeaways
- Monte Carlo turns an un-addable register into a single, comparable exposure distribution.
- Size reserves against tail loss and P90, not the expected average.
- Modelling correlation avoids dangerously understating aggregate exposure.
- Credibility rests entirely on disciplined, documented likelihood and impact inputs.
See This on Your Own Data
AuditPro Core renders this dashboard from your tenant's live, tamper-evident records — every figure traceable to source.