An actuary who builds the environment.
Instead of speculating about what AI means for actuaries, I run experiments and publish the numbers, the failures, and the method. The agent does the case-by-case work; the actuary reviews, approves, and signs. Experience studies, predictive analytics, and interactive things you can poke.
NewOn Notes: The wiki that writes itself — Karpathy's LLM wiki, read as an actuary →The other rooms
Interactive learning materials for SOA Predictive Analytics — decision trees, GLMs, EDA — concepts you can scroll, poke, and play with instead of reading about. Built from the course I teach at the Institute of Actuaries of Korea.
The 2026 AI shifts that matter for actuaries — retold as interactive essays, not hot takes. Note 01: why the harness raises the floor, with a reliability simulator you can flip switches on.
About
HyunSu (HS) Kim, ASA — Senior Actuarial Consultant, Regional Life & Health Pricing, Munich Re Singapore; before that, Munich Re Korea and AIA Life. 15 years in life & health, on both the direct and reinsurance sides of the same data. I led experience studies and assumption setting for seven years, teach predictive analytics and AI at the Institute of Actuaries of Korea, and now work on integrating AI and advanced analytics into actuarial methodologies across APAC & MEA. The question underneath all of it: what should the agent do, and what must stay with the actuary? My answer so far: the agent does the case-by-case work; the actuary reviews, approves, and signs.
Everything on this site is personal work: built on my own setup, on synthetic data, with views that are my own.