Case Studies

Issue - A small insurance company may not have the actuaries on staff to perform the financial reporting required.  ​

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Solution - As a member of the American Academy of Actuary, having successfully attended the Life and Health Qualification Seminar, I can perform the required tasks and satisfy the regulatory requirements. 

Issue - An industrywide issue should be researched, 'to substitute facts for appearances and demonstrations for impressions' as John Ruskin stated

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Solution - I partner with organization to perform research for the benefit of the industry.  "Emerging Risk Survey", "Impact of Criminal History on Life Insurance" provide valuable insights for the profession.     

Issue - Part of the code was written in COBOL or another language that is no longer supported anymore.  And changes are needed.  Without SME around.  Can we feed this into a Large Language Model and get it written in a more modern language?

 

Solution - LLM are great for understanding code.  Not always efficient at writing new code.  But this could provide a good start for the
​rewriting exercise.  Don't forget the appropriate precautions to prevent public sharing of private information.

Issue - Am I bringing bias into my client, as a solution provider to the Insurance Industry? 

 

Solution - Following the bias regulation twists-and-turns, I support vendors to ensure their solution is aligned with upcoming regulation by testing the potential bias in the processes they provide.  A bias compliance certification is issued as a result..

Issue - How to right-size the governance of models, including AI/ML and GenAI models, for your company, based on its size and risk appetite?  OSFI E-21, AML draft on model risk management, as well as the NAIC AI Bulletin and various US States documents, are raising the bar for model risk management   

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Solution - We work together to find the right solution based on the risk and complexity of the models in your company.

Issue - AI/ML models' special characteristics trigger revisiting the model risk management framework.  And you don't understand where the wind is blowing, where NIST, NAIC Model Bulletin, Biden executive order, and various State regulations are pointing to?  How to make progress on the AI journey without risking major re-work when the regulatory framework crystallizes?    

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Solution - I follow the twists and turns of the US regulations, Canadian, and European regulations to understand where the wind is blowing.  Working assumptions can be easily articulated and reaffirmed once the regulatory thought process matures, allowing us to reap the benefits of AI securely.