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Towards Open Collaboration in Insurance Analytics

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Recent advances in AI technology have had, and will continue to have, profound impact on all industries, including insurance. However, insurance analytics professionals, and actuaries in particular, being some of the most quantitatively minded businesspeople, have a unique opportunity to embrace AI and open research and modernize the profession. We introduce Kasa AI, a not-for-profit community initiative for open research and software development for insurance analytics. Inspired by rOpenSci and Bioconductor, we hope to bring together the insurance community to solve the most impactful problems.

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Towards Open Collaboration in Insurance Analytics

  1. 1. Towards open collaboration in insurance analytics EARL 2019 @kevinykuo
  2. 2. Amuse-bouche About me! - Kevin (he/him) - Currently: Multiverse team @RStudio - Formerly: Actuary, “data scientist” - Likes to drink taste wine
  3. 3. Amuse-bouche About me! - Kevin (he/him) - Currently: Multiverse team @RStudio - Formerly: Actuary, “data scientist” - Likes to drink taste wine - Wrapping up a book on Spark + R!
  4. 4. Towards open collaboration in insurance analytics EARL 2019 @kevinykuo
  5. 5. A+ for effort!
  6. 6. Japanese for umbrella (I’m not Japanese) Umbrella, because insurance The (on-going) story of... AI, for hype
  7. 7. Menu of the day Who we are / What we do Château Kasa, Blanc de Blancs, “Le Rêverie”, NV Lessons learned / Discoveries / Tips Bad Joke Cellars, Red Blend, “The Cynic”, 2012 Current projects / Vision Dr. Brandstiftung, Riesling S.G.N., “Einladung”, 1990
  8. 8. Menu of the day > Who we are / What we do Château Kasa, Blanc de Blancs, “Le Rêverie”, NV Lessons learned / Discoveries / Tips Bad Joke Cellars, Red Blend, “The Cynic”, 2012 Current projects / Vision Dr. Brandstiftung, Riesling S.G.N., “Einladung”, 1990
  9. 9. Tl;dr We aspire to be like... ...but for insurance
  10. 10. The output - Research (reproducible + open) - Software (pkgs & templates) - Educational content (tutorials/blog posts)
  11. 11. Data scientist / software engineer Actuary “Business stakeholders”
  12. 12. Menu of the day Who we are / What we do Château Kasa, Blanc de Blancs, “Le Rêverie”, NV > Lessons learned / Discoveries / Tips Bad Joke Cellars, Red Blend, “The Cynic”, 2012 Current projects / Vision Dr. Brandstiftung, Riesling S.G.N., “Einladung”, 1990
  13. 13. #1 Empathy
  14. 14. Acknowledge the Objections
  15. 15. Towards open collaboration in insurance analytics EARL 2019 @kevinykuo
  16. 16. Towards open collaboration in insurance analytics EARL 2019 @kevinykuo
  17. 17. Towards open collaboration in insurance analytics EARL 2019 @kevinykuo
  18. 18. Cultural changes don’t happen overnight!
  19. 19. wins <<- wins + 0.01
  20. 20. wins <<- wins + 0.01 Build critical mass, slowly but surely
  21. 21. Disrupt respectfully
  22. 22. Disrupt respectfully don’t be a cynical asshole don’t be a hero
  23. 23. “The way things are done now is going to be completely obsolete in 5 years.”
  24. 24. “It’s not epistemic uncertainty unless it’s from the Épistéme region of France, otherwise it’s just parameter estimation uncertainty.”
  25. 25. Mind the gap
  26. 26. Q3-IBNR-Analysis-v1.xlsx Q3-IBNR-Analysis-v2.xlsx Q3-IBNR-Analysis-v2b.xlsx Q3-IBNR-Analysis-v3-FINAL.xlsx Q3-IBNR-Analysis-v3-FINAL-REVISED.xlsx
  27. 27. Q3-IBNR-Analysis-v1.xlsx Q3-IBNR-Analysis-v2.xlsx Q3-IBNR-Analysis-v2b.xlsx Q3-IBNR-Analysis-v3-FINAL.xlsx Q3-IBNR-Analysis-v3-FINAL-REVISED.xlsx Happygitwithr.com Tutorials Etc
  28. 28. The output (old priority) 1. Research 2. Software 3. Educational content (new priority) 1. Educational content 2. Software 3. Research
  29. 29. #2 Build first, then iterate
  30. 30. Nobody: Me: I didn’t say anything about Agile or MVP, y’all did!
  31. 31. - Will people actually participate? - How do we ensure code quality? - Are we using state-of-the-art workflows? - How do we vet projects and contributors? - How do we resolve differing design preferences in the community? - What should our governance structure be?
  32. 32. Nobody:
  33. 33. What is good debt? It's taking money from someone, promising to pay them back more money in the future, and using the original money to make even more money than that, so both you and the lender profit. Good tech debt is just the same. It's taking shortcuts now to save time and get something shipped, knowing that it will cost you more time than you're saving now to fix it later. But by shipping now, you ensure there is a later, with a product that's out there and being used, a bigger team and therefore adequate time and then some to fix the debt and keep improving the product. Everyone wins. @davnicwil https://news.ycombinator.com/item ?id=20685950
  34. 34. Get content out there so people can react to it!
  35. 35. #3 Nights and weekends of a few people only go so far
  36. 36. Tl;dr We aspire to be like... ...but for insurance
  37. 37. Tl;dr We aspire to be like... ...but for insurance
  38. 38. We’re actively seeking support - Research/education grants through actuarial societies - Partnerships with companies - Partnerships with academic labs / departments - More outreach to the R and insurance communities
  39. 39. - Be cool (but not too cool) - Just do it - Make it rain
  40. 40. Menu of the day Who we are / What we do Château Kasa, Blanc de Blancs, “Le Rêverie”, NV Lessons learned / Discoveries / Tips Bad Joke Cellars, Red Blend, “The Cynic”, 2012 > Current projects / Vision Dr. Brandstiftung, Riesling S.G.N., “Einladung”, 1990
  41. 41. Ongoing Project: Practical Ratemaking - End-to-end tutorial on general insurance ratemaking - Covers data prep, modeling, filing, implementation - Everything is reproducible!
  42. 42. Leveraging R’s reproducible workflow ecosystem to run a distributed data science project...
  43. 43. Keeping track of moving pieces with
  44. 44. Capturing package dependencies with renv https://rstudio.github.io/renv/ renv.lock
  45. 45. Written in Built on Continuously deployed to
  46. 46. Get involved! Other ongoing/upcoming projects include - Deploying real-time models developed in R with k8s - Underwriting dashboard featuring ML explainability - Data simulators - And more!
  47. 47. The invite Please connect if you are - A useR at all interested in insurance - Insurance student/professional at all interested in R/open source - Interested in helping out in any way - An oenophile
  48. 48. Cheers! Resources - github.com/kasaai - kasa.ai / blog.kasa.ai / quests.kasa.ai - slack.kasa.ai - Hit me up @kevinykuo (github, twitter, linkedin)

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