Aug 18, 2026 · 24 min · 9 segments
Are You Ready for the Next Unknown Known? Relying on rules of thumb and past trends is a fast track to getting blindsided. From climate feedback loops to high-velocity risks, here is why traditional…
Max RudolphHost
Dave IngramHost
An emerging risk generates uncertain results that are hard to recognize and model.
This can be due to cognitive bias, for example, when a recurring risk like a pandemic or earthquake hasn't happened lately, like recency bias.
Third method is when a risk evolves, like global warming, leading to extreme events, tipping points, and feedback loops.
Emerging risks and general uncertainty require analysts to incorporate a margin of safety into their assumptions.
Each emerging risk contributes to what I call an unknown known, where historical data is no longer predictive.
Stakeholders anchor using another cognitive bias on statistical data to model the future.
Few risks today generate assumptions that are not evolving in some way, either from novel outcomes, nonlinear trends, or discontinuities.
It's important for models to consider emerging risks when viewing the impact of tail events.
Model analysis depends on the time horizon, severity, and velocity of the risk impact.
A risk like the sun's eventual death or a large meteor impact can be ignored since the result overwhelms all outcomes.
A likely risk with minimal severity can be ignored or built into the base assumption, while those that can quickly accelerate like an earthquake should be included due to velocity.
Liquidity risk exposure is a good example where creativity should be considered since the risk builds quietly and can quickly expand into a high severity event.
Evolving risks are the most common, but corporate culture may push back after discovery.
For example, interest rates have ranged from a few basis points to nearly 20% just in my lifetime.
Why are we surprised when they reach typical extremes? Foresight is the key to success when evaluating emerging risks across longer time horizons.
Scanning for these risks is hard, but becomes more familiar for those who already have a process on their radar.
Many risk managers miss changes because they rely on rules of thumb for frequency and severity from historical data, rather than using scenario testing and first principles to generate a range of outcomes that align with the desired risk appetite.
Insurers need to consider cash flow mismatches between assets and liabilities across a variety of time horizons.
If a liability has a short duration guarantee, like car insurance, the assets should be liquid but extend with little interest rate risk.
An emerging risk generates uncertain results that are hard to recognize and model.
This can be due to cognitive bias, for example, when a recurring risk like a pandemic or earthquake hasn't happened lately, like recency bias.
Third method is when a risk evolves, like global warming, leading to extreme events, tipping points, and feedback loops.
Emerging risks and general uncertainty require analysts to incorporate a margin of safety into their assumptions.
Each emerging risk contributes to what I call an unknown known, where historical data is no longer predictive.
Stakeholders anchor using another cognitive bias on statistical data to model the future.
Few risks today generate assumptions that are not evolving in some way, either from novel outcomes, nonlinear trends, or discontinuities.
It's important for models to consider emerging risks when viewing the impact of tail events.
Model analysis depends on the time horizon, severity, and velocity of the risk impact.
A risk like the sun's eventual death or a large meteor impact can be ignored since the result overwhelms all outcomes.
A likely risk with minimal severity can be ignored or built into the base assumption, while those that can quickly accelerate like an earthquake should be included due to velocity.
Liquidity risk exposure is a good example where creativity should be considered since the risk builds quietly and can quickly expand into a high severity event.
Evolving risks are the most common, but corporate culture may push back after discovery.
For example, interest rates have ranged from a few basis points to nearly 20% just in my lifetime.
Why are we surprised when they reach typical extremes? Foresight is the key to success when evaluating emerging risks across longer time horizons.
Scanning for these risks is hard, but becomes more familiar for those who already have a process on their radar.
Many risk managers miss changes because they rely on rules of thumb for frequency and severity from historical data, rather than using scenario testing and first principles to generate a range of outcomes that align with the desired risk appetite.
Insurers need to consider cash flow mismatches between assets and liabilities across a variety of time horizons.
If a liability has a short duration guarantee, like car insurance, the assets should be liquid but extend with little interest rate risk.
The rest of this transcript — segmented and speaker-labeled, so you land on the exact moment something was said
Search every transcript — by keyword, by phrase, or by meaning, across every show Radar indexes
Trends — what is surging across podcasts, measured against its own baseline
Alerts — when a name you follow appears in a newly indexed episode
No account is needed to search Radar.
