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Next "big thing" in analytics?

I recently have been working for a client who develops predictive models for the property and casualty insurance industry. Is this really the next "frontier" of analytics? You know, like database marketing was 10 years ago?

There seems to be big demand for the quantitative master's or PhD level person who wants to do pricing, underwriting and claims models.

Would this be attractive to a good statistician? Is "an entrepreneurial statistician" an oxymoron? I appreciate everyone's thoughts

Tags: insurance, modeling, statstician

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Permalink Reply by Tim Manns on February 2, 2009 at 12:33am: The big problems with analysing insurance claims is that there is often a lack of structured data, the main issue being that claims are often in written format and read by an 'emotional' and inconsistent group of numerous humans. Text Mining or similar analysis of the claim text itself is required for speedy and impartial mass processing. This is certainly a fairly new avenue and offers some opportunity for insurance companies to optimise work force (yes, cut work force...) and reduce (or identify) frauduent insurance claims.

- Tim Manns; ▶ Reply to This

Permalink Reply by J. Matthews on March 18, 2009 at 3:45pm: This is great news for me. Just two days ago, I got a phone call from a recruiter for a well-known P/C insurance company. I am in the interview process for an actuarial internship in a predictive modeling role. Apparently, the company is really expanding in this area and building predictive modeling teams. I have a B.S. degree in actuarial science, and from what I've read on this discussion, it's a good thing after all I didn't get a full-time job after graduation, and went to grad school instead for an M.S degree in applied stats. I just finished my second quarter yesterday, which included a SAS-based course on logistic regression and loglinear analysis. So, I very much thank all the previous posters for their input.; ▶ Reply to This

Permalink Reply by Marybeth Haas on March 19, 2009 at 8:20am: Congratulations J. Matthews - I think you're off to a very successful and lucrative career. As an update from my perspective - as a recruiter who specializes in analytics and statistics - the insurance field is still growing and many of the top tier (stable, no bad debt - NOT AIG) type companies are looking for predictive modeling talent.

Here's my commercial - CALL or Email me if this is an attractive career path for you! 312-265-6280 or marybeth@martinpartners.com; ▶ Reply to This

Permalink Reply by John Johnson on April 24, 2009 at 10:00am: Without entrepreneurial statisticians, we'd still be counting out means on our abacuses. (No standard deviations.); ▶ Reply to This

Permalink Reply by Richard Boire on June 26, 2009 at 5:39pm: From the below blogs, there seems to be all this hype about statisticians and math and that perhaps actuaries do have the right skillsets. Make no mistake, there is much more to this issue than just math and stats.
I do believe that like in all data mining and analytics projects, it is less about the math(albeit it is important) and more about the data and how to work with it. This is the value-add and the reason why firms such as ourselves are engaged in this area in Canada. If you do not understand data and the proper discipline in dealing with it and yes there is a science to it, then you are setting yourself up for disaster.

I know many of you may disagree with me and yes I do have a love of math and science and how it can be applied to business problems, but the key to success is understanding the data.; ▶ Reply to This

Permalink Reply by Vincent Granville on June 27, 2009 at 1:33am: Simple or even rudimentary (but robust) statistical model with deep domain knowledge does much better than very sophisticated predictive model combined with poor data or lack of domain expertise.; ▶ Reply to This

Permalink Reply by John A Morrison on June 27, 2009 at 12:34am: yes; ▶ Reply to This

Permalink Reply by John A Morrison on June 28, 2009 at 6:25am: Just to be clear i was agreeing wholeheartedly with Richard Boire's comment - wholeheartedly, let me give an example with which I am struggling right now. The governments all over the world are pumping huge (the word does not depict the size) amounts of money into the financial system right now and long term interest rates are rising precipitously, the logical conclusion is that money supply is ballooning and yet official money supply data both in the US and Europe is so noisey that one cannot clearly see M3 growing not as clearly as one can see in rates - thus one has to be apply a wee bit of mathematical trickery (statistical analysis) to these vectors to get the real picture, in effect turn down the noise control and extrapolate the trend, that is what we do, put simply, is it not?; ▶ Reply to This

Permalink Reply by J. Matthews on June 27, 2009 at 12:50pm: I am now working as an intern with the company I mentioned that interviewed me. So far, I really enjoy what I'm doing and learning. I do agree with the posts above, but it seems my temporary employer is building a team of actuaries or actuarial students with skills in statistics software programming, e.g. SAS. In other words, their goal is to build a team with deep knowledge of both the data and the process of constructing predictive models. Better than having one skill or the other is having skills in both.; ▶ Reply to This

Permalink Reply by Joe Morrison on July 9, 2009 at 11:13am: J. Matthews--Would you be open to learning about an innovative way to give your team more access to your data and bring back your queries in seconds? I work for a company in Netezza that is the leader in this space.; ▶ Reply to This

Permalink Reply by J. Matthews on July 9, 2009 at 5:06pm: I think my company's data management and access tools are pretty well established, and query response times are not an issue. Also, as an intern, I feel I'm not in the position of suggesting to my boss and managers a different way to access data.; ▶ Reply to This

Permalink Reply by Patrick Nicolas on June 28, 2009 at 2:35pm: I think it would qualify as the next frontier of analytics because it requires a rare combination of domain expertise, large scale computation (cloud computing), AI and statistical inference.; ▶ Reply to This