Predictive Modeling–Business and Consumer Algorithmic Data Screening Parameters Used With Insurance Underwriting–Sam
Posted Jun 13 2012 3:41pm
Ok , if you read here often enough, I have been telling you this for the last 2 years and it’s all about risk assessment and the algorithms used to calculate scores. I understand the need and use of a lot of this technology but on the other side it gets abused and we have tons of flawed data floating around out there and it’s getting worse every day. For what ever reason, data is flawed, be it someone lies, or data is not updated, or someone creates software with a bunch of algorithms to sell more software and yes that last one occurs more than you may think. Even the author of this article states how “scary” the consumer data is, again as there’s flaws but too many naïve internet readers believe they see and those who create some of these formulas, even though they may not admit it, cash in and make money off the consumer’s backs.
Also as the article states, it’s already being used for screening and again if you have read here for the last 2 years, you know that in what I have blogged about. I used to write code and have 25 years of sales and marketing in my background and am a strange hybrid, but you know what, I can relate the two and predict as when you know the mechanics and have spent enough years in selling, well it’s a good combination, that is if you want to know about what algorithms do and pretty much figure out what direction folks are going, not hard if you know sales, mechanics and read the news.
The one example in here is perfect with seeing if a TV is left on all day…person would be seen with a potential to develop diabetes due to a sedentary lifestyle, but it’s not him/her, it’s the kids…<got to love some of the flawed analytics that’s building out there>. Companies don’t update their licenses with states that mine this data so as a consumer if they don’t update, guess what, its takes you months, maybe a year to get errors off your record…all comes back to flawed data. NYU professor Siefe, a mathematician agrees with me in the fact that we don’t have enough people out there who know how to work with “flawed data” and I might say we might not have enough either to work with non-flawed data either. He wrote the book “Proofiness, the Dark Arts of Mathematical Deception” and yes folks it lives out there, more than what you think. Again know how someone could write code to disguise and market, that’s where I’m coming from. They do it.
So if you are buying life insurance, these folks want the “best in class” for goodness sakes so all the rest of us that don’t qualify are discarded by use of Killer Algorithms.
They all want to predict your mortality and frankly some of us may not want to know that. Check out this link below and watch this video. “Previvor” if you get the disease, you get off the island and this is addressing predictive behaviors and sub clinical screenings. Let’s face it, do you like this? Granted there’s some very good use, but some are going overboard and we still have to function like humans. This is where they are going with some of this and some folks have no clue on balance, especially when “desired” results have a tendency to trump accuracy, and the naïve public still buys in here, go figure.
Now if you are still reading, let’s see how much money (billions) is made by corporations, banks, high frequency companies and more with all this data. Ahem…Walgreens made just short of $800 million in selling data only in 2010 on their SEC report, does that tell you this is a money game and not always in your best interest. I said we need to tax these billionaire companies and help out the consumer who’s getting screwed with flawed data where we are guilty and have to prove innocence on “flawed data”. Why do you think companies don’t build and open new factories to product tangibles in the US when they can hire a few geeks to write some algorithms and mine data and make millions with hardly any employees, code does it all so let’s reduce the value of some of these algorithms and boost the value of “humans” for goodness sakes!!
There’s absolutely a glaring example of what credit folks do with creating software to sell more software. See this FICO example..mismatched data and yet they create all kinds of reports to substantiate selling their algorithms, how dumb can we be and suck all this up? This is just like that example of the kids watching TV, flawed and just made up to make money off the middle class.
So where are the “predictive models” for the Facebook IPO I ask <grin>. Their analytics came back to bite in the form of some “rogue algorithms”…and we know the rest of that story…Killer Algorithms attack once again. CEO was less than knowledgeable and again just one more of those high paid figureheads today out there running around with little tech knowledge. If I were a software engineer at Facebook, I would be pretty angry and not to mention inquisitive too as what happened with those algos as they write and test enough of their own.
Back on track, not too long ago Accretive proved how important algorithms are to making money, remember that one where employees were collecting at the ER room and doing some very unethical collecting, all while on pay for performance for bring in the money? So when you think about underwriting, these companies are doing the same thing, money, money and more money and ethics suffer so underwriting stands to get just bit more polluted as time moves forward and gee, what’s going to happen when very few can meet the parameters that are added to all the algorithms? Will nobody quality? No, they will just created new algorithms and play God with flawed data and formulas more than likely as nobody calls them on it sadly.
In summary it looks like the underwriting business is taking the same algorithmic path that Wall Street did with our finances except this time we can see it all, that is if we “choose” to do something about unethical parameters with algorithms. Maybe JP Morgan has a comment or two to add there? <grin> BD
ANAHEIM, Calif. – “Predictive modeling” is coming to life underwriting, and while it may help reduce underwriting costs, the approach may be confusing to clients or even a touch scary, says a long time underwriter. Advisors will therefore need to be ready to educate on this.
Predictive modeling refers to systematic analysis of data, including historical information. In the life underwriting context, this data is about consumers.
Analysts use the information to create predictions, or models. Underwriters can apply the information to life insurance applicants in order to determine their risk classification for the insurance coverage they are seeking.
Advisors may be familiar with the approach in other, non-insurance contexts. For instance, predictive analytics have been used in credit risk scoring, medical research, consumer marketing and even meteorology, the underwriter said. “iTunes uses it through its Genius application to provide song recommendations; Netflix does the same for movies.”
Predictive models fall into different categories. Phelan considers one of them -- the consumer data model — as having “a little scary” side to it.
The consumer data model involves applying information obtained about an applicant from various databases and information resources. The data might show things such as the applicant’s online purchases, magazine prescriptions, TV watching behavior and leisure activities, the underwriter said.