Likelihood and impact scoring

Category: Risk management frameworks · Reviewed by Jake Leat, Associate Director · Last reviewed

Likelihood and impact scoring

Likelihood and impact scoring is the qualitative or semi-quantitative process of placing identified risks on a defined scale of probability and consequence, in order to prioritise them within a register or matrix.

Principles for credible scoring

1. Anchor the scales. Every band must be defined in measurable terms (annual probability, £ loss, days of downtime). Undefined labels (“possible”, “high”) produce inconsistent results across raters.
2. Score the event, not the category. “Cyber risk” cannot be scored; “ransomware encrypting the policy admin system causing 5 days of business interruption” can.
3. Score inherent and residual separately. The difference shows the value of existing controls.
4. Use multiple raters. Independent scores from underwriting, claims, IT and finance, then reconciled, reduce single-rater bias.
5. Calibrate to historical losses. If your insurance industry data shows a peril occurs once every 8 years, do not score it as “rare”.

Cognitive bias and calibration

Practitioners systematically over-weight recent events (availability bias), anchor on memorable numbers and confuse “I don’t know how often” with “rare”. Douglas Hubbard’s How to Measure Anything and the IFoA’s risk literature both recommend periodic calibration training — exercises in which raters estimate well-known probabilities and impacts and then check their accuracy.

References

• IEC 31010:2019.
• Hubbard, D. (2014). How to Measure Anything: Finding the Value of Intangibles in Business.
• IFoA (various). Risk Management — Practical Guides.

Cross-references

• Risk matrix
• Inherent risk
• Residual risk
• Quantitative risk assessment

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