Estimable Risks
Some organizational risks can be estimated from recurring events and historical records; others require additional evidence and judgment. Banasiewicz proposes combining information sources through the 3E framework to assess likelihood and impact, while treating the results as estimates rather than certainties.
THE IDEA
Estimable Risks
Risk management involves identifying adverse events, estimating their likelihood and impact, and deciding how to respond. The author’s historical sketch traces commercial risk management to 2000 BC, describes risk pooling and transfer developing in the 17th century, and links the Great London Fire of 1666 to the expansion of property insurance. Risk management now covers many operational and strategic concerns, including accidents, liability, finance, security, and project delivery.
Enterprise risk management (ERM) considers these risks together. Its sequence is to identify, estimate, map, and respond to risks within an organizational risk profile. Considering a portfolio can reveal interactions that separate assessments miss. The scope can include recurring losses, events that have not yet occurred locally, and uncertainty involving possible gains as well as losses. The source names ISO 3100 and COSO 5 as prominent frameworks. Banasiewicz argues that such frameworks provide less guidance on estimating risks than on identifying and organizing them, and proposes the 3E framework as a way to bring relevant evidence to that task.
The starting point for the 3E framework is to ask what is known and what additional information could be obtained. Different risks call for different sources. Repeated worker injuries may provide accident and insurance-claim records from which to estimate future frequency and severity, provided conditions remain comparable. An irregular threat such as wildfire may require additional evidence and expert judgment because local historical records are limited.
Using the 3E framework means matching the information needed for a risk to sources capable of informing it. The relevant dimensions of the 3E framework can then guide analysis. Combining sources may improve an estimate, but it does not remove uncertainty about rare events or changing conditions.
When to use it
Estimating the frequency and cost of equipment failures in a manufacturing plant to prioritize maintenance investments.
Assessing the likelihood and financial impact of a data breach for a tech company to set cybersecurity insurance coverage limits.
Evaluating the probability of supply chain disruptions due to geopolitical events to develop contingency plans.
Quantifying the risk of customer churn for a subscription service using historical customer data and predictive models.
FROM THEORY TO EVERYDAY LIFE
In practice
Worker Injuries as Estimable Risk
A construction company uses historical accident and insurance claim data to estimate the likelihood and severity of worker injuries over the next year. Because such injuries recur with regularity, the company can apply actuarial methods to set safety budgets and insurance reserves.
Wildfire Threat as Less Estimable Risk
An insurer assessing wildfire risk in a region with limited local records considers expert judgment and model-based estimates alongside historical data. It assesses whether those sources support a useful underwriting estimate and where uncertainty remains.
A MOMENT TO REFLECT
Put it to work.
How can your organization distinguish between risks that are easily estimable using historical data and those that require expert judgment or alternative methods?
What sources of information (e.g., accident records, market data, expert panels) could you combine to improve the estimation of a particularly uncertain risk?
How could the 3E approach help identify evidence needed to estimate a risk after using a framework such as ISO 31000 or COSO?
References
- ISO 31000: Risk Management — International Organization for Standardization (2009)
- COSO Enterprise Risk Management Framework — Committee of Sponsoring Organizations of the Treadway Commission (2004)
CONTINUE THE CONVERSATION
Source texts
- Evidence-Based Decision-Making How to Leverage Available Data and Avoid Cognitive Biases (Andrew D. Banasiewicz)