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Janot Mendler De Suarez

Apr 21, 2018
06:52

Judge


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If this proposal gets traction, the potential to transform the relationship between insured and insurer from a market-controlled environment to a dynamic individual-as-part-of-the-collective risk and cost management relationship with whatever insurance pool one may participate in. Another exciting dimension that is suggested but not *yet* elucidated is the compounding effect of nesting the compounding ripple effect of enabling insured parties to receive timely risk info and to link learning to manage risk under non-real conditions (through game/VR/AR interface) and to link realtime risk reduction action to premium reductions could effectively bring a more equitable balance into the traditional insurance market by conferring a monetized value proposition to the role of every level of insured. For example, if my neighbours verify taking timely risk reduction action based on a forecast, I can see the result in a slight adjustment to my own as well as my municipality's insurance premium/rate, or when my municipality votes in a new building code I see the 'ripple' in my own premium/rate, as do my neighbours. By the same token, over time all types of risk including at the level of sovereign risk insurance and re-insurance could be inter-linked in a dynamic information and insurance delivery system. The potential for applying machine-learning (self-learning algorithms) to understand and value the relative risk reduction impact of different types of risk management action is an exciting frontier also suggested by this proposal.


Aadhithya Sujith

May 12, 2018
05:31

Catalyst


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@Janot Mendler De Suarez Thanks for your valuable feedback. We have tried to incorporate the suggestion from the community & judges, However we found it challenging to include everything in detail within the character limits. Please note the following changes.

In what are the other benefits section, Now we have made it clear about the "compounding effect of nesting the compounding ripple effect of enabling insured parties to receive timely risk info and to link learning."

"Enabling insured parties to receive timely risk info & to link learning to manage risk under non-real conditions (through game/VR/AR interface) & realtime risk reduction action to premium reductions using pre disaster dashboard".

For "The potential for applying machine-learning (self-learning algorithms) to understand and value the relative risk reduction impact of different types of risk management action is an exciting frontier".

We have incorporated a pre-disaster indicator dashboard, data is the new oil & we plan to leverage this data collected by government authorities in a easy to access dashboard, by selecting a particular location, people & authorities will be able to get all the facilities available, facilities & resources missing, population breakup and other critical details about safety of that location during disaster. This will enable government disaster/ social welfare projects to be implemented in areas that need the most for example it make sense to build hospitals and healthcare facilities in areas that don't have adequate facilities & resources. Later machine-learning (self-learning algorithms) can be used to score each location based on the infrastructure, population, disaster prone zones & variety of other human related factors. These scores will have direct impact on insurance premium rates.

 

Thanks & regards

Aadhithya

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