My Actuarial Working Life

In its recent announcement on medical insurance , Bank Negara Malaysia highlighted Malaysia’s medical inflation rate of 15%, which surpasses the average across global and Asia Pacific region. However, when I consider the rate of increase in medical insurance premiums, I question whether this 15% figure is truly a suitable benchmark – are we really comparing apples to apples?   Before diving into my thoughts on this, I believe it’s helpful to explain the concept of " insured lives " experience.  

Bank Negara Malaysia has announced interim measures to address rising medical insurance premiums. While I can’t foresee how some of these measures will resolve the underlying issues, I am particularly curious to see how the Diagnosis Related Group ("DRG") payment model might help tackle the root cause.   Reflecting on my experience with my first actuarial employer, I recall being responsible for preparing claim analyses for individual medical insurance. One of the more interesting aspects of this work was constructing claim distributions based on actual experience. While the results were typically presented in tables, I often created histograms to better illustrate the distribution – in my opinion, the visual representation provided deeper insights into the claim patterns.  

Medical insurance has become one of the hottest topics in Malaysia’s insurance and Takaful industry lately. Some members of parliament (MPs) have urged Bank Negara Malaysia to impose a cap on rising medical insurance premiums. In the process, certain MPs have portrayed insurance and Takaful companies as profit-driven entities operating at the expense of public interest – with social media posts framing the debate as a "Rakyat Win."   I’d like to share a few general thoughts on this issue, steering away from technical details for now (perhaps I’ll dive into those another time). Here are a few perspectives we can take away from the recent developments:  

In my last post , we discussed the importance of avoiding “black box” Prophet models—models so complex and opaque that users struggle to understand the underlying calculations and dependencies. Without clarity, these models can lead to errors, inefficiencies, and unwelcome surprises when something goes wrong. Today, let’s shift our focus to another characteristic of a "good" Prophet model: keeping redundancy low . Let's make the idea simple, redundancy in a Prophet model refers to variables in the model’s library that aren’t actually used in any products within the workspace. If a Prophet model has high redundancy, this means it has thousands of variables available but only a fraction actively contributing to the calculations. 

If you're working in the valuation team of a life insurance company, chances are you've spent a lot of time with Prophet. It’s that powerful tool we use for things like cash flow projections and calculating reserves. But here's the thing—how often do you really stop to think about whether the Prophet model you're using is a 'GOOD' Prophet model ?

In my last post and debut podcast, I talked about why strong foundations matter for coming up with smart, workable solutions in business. But, there's a twist when we hit a problem that's completely new to us: just having that solid base isn't always enough. This brings me to a story I love to share with my team and my training participants, about how Henry Ford had a lightbulb moment that changed cars forever. Did you know he got the idea to build cars faster and cheaper by watching how meatpacking plants worked? They had this assembly line where everyone did one job, moving the product along quickly. Ford thought, "Why not do this with cars?" And just like that, in 1913, he changed the game by setting up an assembly line at his car plant in Michigan.