Rising Medical Insurance Premiums (4) – Additional Metrics in Claim Analysis

In one of the discussions with my team about medical insurance, I emphasized that claim data is one of the most valuable assets for insurance companies. Even if insurers engage third-party administrators ("TPAs") to handle claim processing, it’s crucial for them to retain access to detailed claim data – and at a granular level.  


Loss Ratio Alone Isn’t Enough 

When it comes to claim analysis for medical insurance, many of us tend to focus solely on loss ratio. While useful, loss ratio analysis primarily evaluates product profitability – offering a high-level view of whether a product is making or losing money. Of course, we can refine this by analyzing profitability across different plans or age groups.  

But here’s the catch – loss ratio alone isn’t enough for medical insurance products that reimburse hospitalization and surgical expenses. Why? Because loss ratio reflects the end result. It doesn’t shed light on the underlying claim experience or the drivers behind those results.  


So, how should we approach claim analysis for medical insurance? I recommend incorporating at least four key metrics – and examining them across different dimensions (e.g., plans, age groups, gender):  

  1. Loss ratio
  2. Incidence rate
  3. Average claim paid
  4. Average length of stay (for hospitalization)

If you studied actuarial science before, you might recall the formula for expected aggregate loss:  

E(S) = E(N) × E(X)

In simple terms, the above formula explains the total claims depends on frequency of claims (how often claims occur) and the severity (how large each claim is).  Incidence rate and average claim paid correspond directly to these two components – frequency and severity.  


Understanding the Drivers of Claims  

When aggregate claims rise – leading to an increased loss ratio (assuming premium rates remain unchanged) – analyzing these additional metrics helps identify the root cause. Is the rise driven by higher incidence rates, larger average claim amounts, or both?

By breaking down the drivers behind rising claims, we gain deeper insight into the underlying claim experience. And with that understanding, we can make more informed decisions – beyond simply reacting to loss ratios.  

As many of us know, increasing premiums alone won’t solve the problem.


Further reading:

  1. Rising Medical Insurance Premiums (1) – Is Emotion Clouding the Discussion?
  2. Rising Medical Insurance Premiums (2) – Why Small Claims and Big Tails Matter?
  3. Rising Medical Insurance Premiums (3) – Is Medical Inflation in General a Fair Benchmark?
  4. Rising Medical Insurance Premiums (4) – Additional Metrics in Claim Analysis


Note: The image is generated using Grok AI.

Comments

Post a Comment

Popular posts from this blog

Other Ways to Prepare Prophet Model Point Files? Try FoxPro!

Image
If you are a frequent Prophet user, you must be definitely familiar with "Model Point Files" ("MPF"), i.e. the policy data that you compile in a specific format that Prophet can recognize as inputs for Prophet runs. If my guess is right, most probably you use Data Conversion System ("DCS") to convert the source data you download from your policy administration system (may be in various format, e.g. fixed ASCII / fixed width, comma delimited, tab delimited, etc.) into the required MPF, based on the definitions you specified in your DCS programs. In case your source data are divided into various files, you may need to carry out more steps to prepare MPF, for example: Open different source files in Excel Combine required fields from different source files by using commands (such as) VLOOKUP, SUMIF & MATCH. You may have automated this process by using VBA. Convert the required files into text files Use DCS to convert those text files into MPF  How...
Read more

How do You Setup Indicators in Your Prophet Model?

Image
In my previous post, I have discussed areas that you may consider in setting up input variables . Before I proceed my discussion on core variables, I would think that it is good for use to take a look on indicators - as indicators play an important role in setting core variables with various formulas (known as "variable definitions" in a Prophet model).  The funny thing about indicators is: it DOES NOT carry any formulas that you need to use in calculating a variable! Basically, they are just some texts that you can combine using different logical operators (AND, OR, NOT), such as "ORD_PAR AND SING_PREM" for a single premium ordinary participating product. 
Read more

How Should I Do My UAT? (1): Program / Spreadsheet / Model

Image
If you are working in the actuarial department of an insurance company, it may not be uncommon to you involving in various testing exercises, which are known as User Acceptance Testing ("UAT") in general. Some of us may think that UAT is only applicable to the policy administration system implementation project (Oops... Do I remind on the non-stop follow-ups from your Project Manager?), but in fact UAT is also needed for the programs, spreadsheet templates or actuarial model we use to perform various actuarial studies - whether they are developed internally or by external consultant. It is particularly important for those tools which we are going to use to perform regular studies. In my view, there is no one right answer for the approach to be used in performing UAT on program / spreadsheet / model - most importantly, the approach that you adopt should allow the required testing to be carried out in a structured manner, and it should not create too much "processes...
Read more