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  #21  
Old 10-13-2018, 12:57 PM
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FactuarialStatement FactuarialStatement is offline
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I’ve been doing modeling/data science my entire career in insurance. My take: 1 does make sense and is inevitable. Data science is not just about better models - it’s mostly about better tools. 1 good data scientist proficient with SQL + shell scripting + any general purpose programming language (and including R in that) can run circles around 10 actuaries with excel (or SAS). I’ve automated a lot of actuaries work away - why do they persist on updating the same spreadsheets manually every quarter? Or manually updating hard coded inputs and running the same 26 SAS programs in an enterprise guide project nobody understands or even knows who wrote? The amount of wheel spinning actuaries do to crank out low quality analyses is pathetic.

The funny thing about this is that instead of educating actuaries in programming languages and systems architecture (iCAS aside - an attempt but pretty weak IMO) so that actuaries can become the data scientists, they will lobby to try and obtain a credentialing barrier to entry to enforce incompetence in the profession. It’s like sorry but you are in no way a recognized authority in statistics and programming - in fact it’s pretty much the opposite.
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  #22  
Old 10-13-2018, 01:02 PM
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Originally Posted by FactuarialStatement View Post
I’ve been doing modeling/data science my entire career in insurance. My take: 1 does make sense and is inevitable. Data science is not just about better models - it’s mostly about better tools. 1 good data scientist proficient with SQL + shell scripting + any general purpose programming language (and including R in that) can run circles around 10 actuaries with excel (or SAS). I’ve automated a lot of actuaries work away - why do they persist on updating the same spreadsheets manually every quarter? Or manually updating hard coded inputs and running the same 26 SAS programs in an enterprise guide project nobody understands or even knows who wrote? The amount of wheel spinning actuaries do to crank out low quality analyses is pathetic.

The funny thing about this is that instead of educating actuaries in programming languages and systems architecture (iCAS aside - an attempt but pretty weak IMO) so that actuaries can become the data scientists, they will lobby to try and obtain a credentialing barrier to entry to enforce incompetence in the profession. It’s like sorry but you are in no way a recognized authority in statistics and programming - in fact it’s pretty much the opposite.


A lot of the legacy knowledge from this profession is useful but there are areas where the cruft is holding us back. We need broader/better ways for actuaries to learn about the new tools available to us. If you don't know about it you can't use it and up with an Excel monstrosity.
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  #23  
Old 10-13-2018, 01:14 PM
hjacjswo hjacjswo is offline
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Originally Posted by FactuarialStatement View Post
Iíve been doing modeling/data science my entire career in insurance. My take: 1 does make sense and is inevitable. Data science is not just about better models - itís mostly about better tools. 1 good data scientist proficient with SQL + shell scripting + any general purpose programming language (and including R in that) can run circles around 10 actuaries with excel (or SAS). Iíve automated a lot of actuaries work away - why do they persist on updating the same spreadsheets manually every quarter? Or manually updating hard coded inputs and running the same 26 SAS programs in an enterprise guide project nobody understands or even knows who wrote? The amount of wheel spinning actuaries do to crank out low quality analyses is pathetic.

The funny thing about this is that instead of educating actuaries in programming languages and systems architecture (iCAS aside - an attempt but pretty weak IMO) so that actuaries can become the data scientists, they will lobby to try and obtain a credentialing barrier to entry to enforce incompetence in the profession. Itís like sorry but you are in no way a recognized authority in statistics and programming - in fact itís pretty much the opposite.
Not sure you are comparable to a regular data scientist coming to work for Allstate after grad school since youve gone through the actuarial exams and understand the methods being use and how to interpret results, etc.

Ive personally always thought an analyst work could be replaced by anybody anyways. You dont need an actuary or a data scientist to do what most analysts do. But the actuaries who are giving them projects and leading them, can they be completely replaced by data scientists?
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  #24  
Old 10-13-2018, 02:49 PM
IANAE IANAE is offline
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Originally Posted by FactuarialStatement View Post
The funny thing about this is that instead of educating actuaries in programming languages and systems architecture (iCAS aside - an attempt but pretty weak IMO) so that actuaries can become the data scientists, they will lobby to try and obtain a credentialing barrier to entry to enforce incompetence in the profession. It’s like sorry but you are in no way a recognized authority in statistics and programming - in fact it’s pretty much the opposite.
My point exactly re rent-seeking objectives. Data scientists have been around a long time and supported many industries - notwithstanding the recent buzz around who they are and what they do - so what does this have to do with the question of merging actuarial societies?

BTW I am a strong advocate for (and a practitioner with) data science knowledge and skills; however I see data science as prerequisite knowledge robust actuaries should have (even if they are not Data Scientists) not vice-versa.
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  #25  
Old 10-13-2018, 11:36 PM
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My point exactly re rent-seeking objectives. Data scientists have been around a long time and supported many industries - notwithstanding the recent buzz around who they are and what they do - so what does this have to do with the question of merging actuarial societies?

BTW I am a strong advocate for (and a practitioner with) data science knowledge and skills; however I see data science as prerequisite knowledge robust actuaries should have (even if they are not Data Scientists) not vice-versa.
Also, no on has explained what part of 'Data Science' leadership is supposedly afraid of (and I haven't heard two people ever give me the same defintion of what a data scientists is anyway).

Is it that they can set up a deep neural net? With point and click tools these days damn near anyone can do that.

Is it that they can set up easy to use data infrastructure? That sounds like corporate IT and Operations problem to be scared of (in my experience).

Is it that they can facilitate the same insight as actuaries without need to adhere to any ethical obligations or standards of practice? That isn't something merging will solve. They operate outside of both societies.

Maybe I can see some of the point of collaborative training efforts, but honestly I'd rather have practical P&C specific training that a series of 'One size fits all' seminars.
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  #26  
Old 10-13-2018, 11:45 PM
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The thing is that I believe the efforts against the data scientists might actually be diluted if they merge. Under the combined society, there will be 5 practices: Life, Health, Pension, P&C, and Others. Others is supposedly made up of banking and finance, but, really, it will be mostly academia. Now, out of these 5 practices, only 2 practices are directly concerned with data science: health and p&c. Maybe some life actuaries care, too, but, definitely not as much as health and P&C. So, you are really talking less than a half of the new organization to really concentrate on data science stuff.
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  #27  
Old 10-14-2018, 02:00 AM
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Originally Posted by FactuarialStatement View Post
Iíve been doing modeling/data science my entire career in insurance. My take: 1 does make sense and is inevitable. Data science is not just about better models - itís mostly about better tools. 1 good data scientist proficient with SQL + shell scripting + any general purpose programming language (and including R in that) can run circles around 10 actuaries with excel (or SAS). Iíve automated a lot of actuaries work away - why do they persist on updating the same spreadsheets manually every quarter? Or manually updating hard coded inputs and running the same 26 SAS programs in an enterprise guide project nobody understands or even knows who wrote? The amount of wheel spinning actuaries do to crank out low quality analyses is pathetic.

The funny thing about this is that instead of educating actuaries in programming languages and systems architecture (iCAS aside - an attempt but pretty weak IMO) so that actuaries can become the data scientists, they will lobby to try and obtain a credentialing barrier to entry to enforce incompetence in the profession. Itís like sorry but you are in no way a recognized authority in statistics and programming - in fact itís pretty much the opposite.


Data science is there to give us actuaries better tools and information. It is incumbent on us to embrace that, seek it out, and help those people help us do our jobs better. Data scientists are not going to be the people who also develop coverage level knowledge of products, who look at the data and realize why some fields may be inappropriate to use, who look at model results and their applicability to the business and recognize problems in use and/or implementation. They won't have the deep, intimate knowledge about concerns other departments have and how to effectively address those concerns, or work with state regulators to explain why a model is actuarial sound. That's supposed to be our job.

I'm sure we all do a lot of things "because it's always been done this way" or "because I don't know if a better way." It seems silly to on one hand espouse the value of specialization as for why we shouldn't merge actuarial societies, then turn assertions and day "we need to be masters of all things dealing with insurance and higher math." Let's let data science help us get the tools and data we need to be able to do higher level analyses, let's focus on developing our skill sets and knowledge base to take advantage of that information once it becomes available.
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  #28  
Old 10-14-2018, 03:49 AM
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It seems silly to on one hand espouse the value of specialization as for why we shouldn't merge actuarial societies, then turn assertions and day "we need to be masters of all things dealing with insurance and higher math." Let's let data science help us get the tools and data we need to be able to do higher level analyses, let's focus on developing our skill sets and knowledge base to take advantage of that information once it becomes available.
I'd argue that the points are fairly different. The merger of two societies in which there is concern over one's ability to meet the needs of the other's members to the quality those members demand, is not comparable to the point that actuaries have always had their roles defined by the unique combination of technology, statistics, and business savvy to support the profitability and solvency of the insurance industry.

I agree data scientists/IT should be embraced in the tools they can bring. But the fear of DS coming into field is more about they expansion past tool design/support and into the analytical decision making. At that stage they are more like practicing actuaries except without the professionalism.

Also, not all DS and not all actuaries and not all of anything because every person and company is unique.
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  #29  
Old 10-14-2018, 07:40 AM
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Originally Posted by Anxiousele View Post


A lot of the legacy knowledge from this profession is useful but there are areas where the cruft is holding us back. We need broader/better ways for actuaries to learn about the new tools available to us. If you don't know about it you can't use it and up with an Excel monstrosity.
???

cruft

noun
Charles. 1852Ė1938, British dog breeder, who organized the first (1886) of the annual dog shows known as Cruft's
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  #30  
Old 10-14-2018, 08:25 AM
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He most likely meant "craft."
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