Decision Errors in Catastrophe Modelling
Definition
Catastrophe loss modeling requires rigorous validation of inputs, outputs, and assumptions to avoid decision errors in reinsurance purchasing and capital allocation. Failure to document data transfers, reconcile outputs, or assess model limitations results in unreliable PML estimates and unsuitable reinsurance structures.
Key Findings
- Financial Impact: AUD 10-50M+ in unreserved losses per major event due to model discrepancies; global AAL $152B with Australia exposed to cyclone/earthquake/bushfire[1][4]
- Frequency: Annual, per modelling cycle or reinsurance renewal
- Root Cause: Manual data handling, lack of controls for model inputs/outputs, unassessed non-modelled perils like bushfire/flood
Why This Matters
The Pitch: Claims Adjusting and Actuarial Services firms in Australia 🇦🇺 risk millions in underreserved losses from model errors. Automation of data validation and reasonableness checks eliminates this risk.
Affected Stakeholders
Actuaries, Underwriters, Reinsurance Managers
Deep Analysis (Premium)
Financial Impact
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Current Workarounds
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Methodology & Sources
Data collected via OSINT from regulatory filings, industry audits, and verified case studies.
Related Business Risks
Cost Overrun from Loss Adjustment Expenses
Capacity Loss from Model Uncertainty
Fehlentscheidungen bei Tarifindikation durch unzureichende, nicht standardisierte Aktuariatsdokumentation
Überhöhter manueller Aufwand bei der Erstellung von Aktuariatsunterlagen für Tarifgenehmigungen
Experience Rating Premium Surcharges
Manual EMR Calculation Delays
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