CLCM in a Nutshell

CLCM In a Nushell
CLCM In a NushellIn my last post, I talked about some of the challenges I see in the current claims modeling efforts, and offered the opinion that Claim Life Cycle Modeling (CLCM) is an approach that helps resolve many of those problems.
In this post, I want to shed a bit more light on CLCM:

CLCM is a strategy

When performing claims modeling, one of the first questions is, “What kind of model will we build?”  The answer to this must align with current corporate strategy.  The model has to be able to answer questions that are both relevant and actionable.  CLCM is a strategic choice to build a flexible framework based on as much available data as possible to answer a wide variety of pricing, reserving, and claims modeling questions.

CLCM is a process

Rather than building a single claims model that attempts to predict a particular future outcome, CLCM involves building a set of interrelated models that form the framework for a prediction of many future claim behaviors.  The result is a probability distribution of a variety of future claim statistics at the claim level, in the aggregate, by segment, by layer, etc.

CLCM is open and transparent

CLCM is an idea that’s been in development at Gross Consulting for over a decade.  During this period, we have delivered numerous presentations and participated in many discussions of the CLCM process, at both regional and national actuarial conferences.  From the outset, the goal has been to encourage open discussion and review of the CLCM process, and to encourage more actuaries to use some of these ideas to enhance their analyses.

CLCM is implemented in software

Here at Gross Consulting, we perform CLCM analyses with the benefit of of specialized software:  Cognalysis CLCM.  However, there’s no requirement that a CLCM analysis be performed using Cognalysis software; we have documented the process thoroughly enough that you should be able to replicate many of the ideas using your own logic.  Of course, we also invite actuaries to leverage our investment of time, effort, and experience to arrive at the finished product much faster.   CLCM is something we believe every practicing casualty actuary should be utilizing.

CLCM unifies pricing, aggregate reserving, and claims modeling

These three analytics efforts rely on the same underlying bodies of data:  past premium, exposure, and claims data.  However, they typically go about formulating the key questions differently, resulting in differing assumptions, and therefore potentially conflicting results.  CLCM, on the other hand, builds a set of claims behavior models that describe future outcomes, resulting in
  1. A pricing model which predicts pure premium at the policy level
  2. A claim-level reserve estimate, including probability distributions that can be rolled up by segment and layer
  3. A claims model that can be used for live claims triage, “jumper” assignment, etc.
Using CLCM, these efforts don’t require three sets of analysts building three separate models – these three deliverables are a natural outcome of a single CLCM analysis, based on the same starting data and a common set of assumptions, so the three models will be in agreement each other.

CLCM builds reserve estimates at the claim level, based on all available information for that claim

Traditional reserving methods look at claim development using triangle methods, which incorporate just three two pieces of information:  Loss, Time Period, Development Age.
Claims Models typically incorporate many more pieces of data, but typically make point predictions as of a particular point in time – say 30 days or 90 days.
CLCM looks at all claim behavior over time, at each time step, with behavior in each time step a function of behavior in the previous steps.

CLCM succeeds when other methods are most likely to fail

CLCM was originally developed to address a very common question:  “How do I best estimate aggregate reserves when things are changing?”  (Or worse yet, “How do I know whether or not things are changing?”)
Traditional triangle methods work well — until they don’t.  Because they rely on just three pieces of information (Loss, Time Period, Development Age) they break down when the book is changing over time across a different dimension.  These scenarios include:
  1. Mix shifts
  2. Changes in case reserving methods
  3. Changes in payment timing (deliberate or not)
  4. Changes in the external environment (trend, new causes of loss)

CLCM in more detail

Over the course of the next 11 weeks, I’ll be diving deeper into many of these ideas, as well as describing some of what I’ve seen as the key features and benefits of CLCM.  I’d like to be explicit with my goal in this series: If you’re an actuary about to start a pricing, reserving, or claims modeling project, you should absolutely look into CLCM as one of your strategies.   Compared to more traditional approaches, the benefits and capabilities CLCM provides are significant.
Please reach out to me with comments or questions at

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