Actuarial and Data Analytics

Providing information and advice on real problems

Reserve(ML) – Advanced Claim Reserving Techniques

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Automate and Improve Your Reserve Estimates With Our BETA Version of Reserve(ML)

Improve your reserve estimates using advanced techniques that go beyond traditional triangle analysis.  Reserve(ML) is a cloud based claim reserving platform tailored for Health Actuaries.  To get a peek of what it can do and see how well it can improve your estimates, I have released a stripped down version for the public.  A more robust version will be available later this year (once we gather all the feedback from our testers).

Prior to releasing the full version to a select few, we want your input on what you find valuable.  As such, we released a high level version to illustrate how it works.  We are looking for feedback, such as what charts, graphs, and data sets are you interested in seeing in your data.

Why Reserve(ML)? 

Actuaries often use various triangle-based methods such as the Development and the Paid Per Member Per Month (PdPMPM) to set reserves. These methods in principle attempt to perform pattern recognition on limited information contained within the triangles.  Even though these methods continue to serve actuaries well, information is being left out that could enhance the overall reserve estimate.

To make up for the lack of information used to estimate the reserves, an actuary relies heavily on his/her judgment. Although judgment is invaluable, biases and other elements can come into play leading to large variances, and the need for higher reserve margins.



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