One of the challenges with property insurance is pricing catastrophe potential where long term credible data does not exist for the current and future infrastructures. Models are attempting this task, but it is clear from the fire events in California over the past two years that even the most sophisticated machine learning may not provide a viable solution.
Yes, totally agree with you. Working in the Credit, Political Risk and Bonds area, I think our current data set is not large and sophisticated enough to price everything. So bad project does present bad risk.
Completely agree, but I’d also like to add it’s even more difficult to rate those property policies even when you think you’ve rated it appropriately — say a flat deductible in most scenarios, but a percentage deductible in the event of a CAT — and then the state chimes in during/after a CAT and mandates carriers don’t apply those percentage deductibles and instead apply the flat deductible.
There are no bad risks? Perhaps. But, there are bad people. Bad people make bad risks, and until somebody figures out how to change human biodiversity, bad people will continue to make a terrible mess of things. Perhaps one in twenty people have an anti-social personality disorder and they are to be avoided. You don’t want them as customers, spouses, friends or suppliers.
Bad risk is a subjectrive term. What does it mean and how is it used. Normally, a bad risk is seen as one that has too many losses that frequently exceed the premium paid. But that is a subjective meaning and standard. The author fails to clarify what he means by bad risk.,
As to the idea that premium offsets risk that is objectively incorrect. When loss is certain premium has limited appeal. When loss is certain and catastrophic the risk pooling arrangement generally fails completely.
One of the challenges with property insurance is pricing catastrophe potential where long term credible data does not exist for the current and future infrastructures. Models are attempting this task, but it is clear from the fire events in California over the past two years that even the most sophisticated machine learning may not provide a viable solution.
Yes, totally agree with you. Working in the Credit, Political Risk and Bonds area, I think our current data set is not large and sophisticated enough to price everything. So bad project does present bad risk.
Completely agree, but I’d also like to add it’s even more difficult to rate those property policies even when you think you’ve rated it appropriately — say a flat deductible in most scenarios, but a percentage deductible in the event of a CAT — and then the state chimes in during/after a CAT and mandates carriers don’t apply those percentage deductibles and instead apply the flat deductible.
There are no bad risks? Perhaps. But, there are bad people. Bad people make bad risks, and until somebody figures out how to change human biodiversity, bad people will continue to make a terrible mess of things. Perhaps one in twenty people have an anti-social personality disorder and they are to be avoided. You don’t want them as customers, spouses, friends or suppliers.
Good point Vox!
Bad risk is a subjectrive term. What does it mean and how is it used. Normally, a bad risk is seen as one that has too many losses that frequently exceed the premium paid. But that is a subjective meaning and standard. The author fails to clarify what he means by bad risk.,
As to the idea that premium offsets risk that is objectively incorrect. When loss is certain premium has limited appeal. When loss is certain and catastrophic the risk pooling arrangement generally fails completely.
Just make sure that your crystal ball is well polished.