Medusa

How Big Data Is Fighting Insurance Fraud

<p style&equals;"text-align&colon; justify&semi;">Everywhere you turn&comma; Big Data is touted as the solution&comma; or at least the means to arrive at the solution&comma; for so many issues and problems facing us today&period; It seems that&comma; if applied properly&comma; there&&num;8217&semi;s nothing that a good heaping helping of Big Data can&&num;8217&semi;t solve&period;<&sol;p>&NewLine;<p style&equals;"text-align&colon; justify&semi;">Now it&&num;8217&semi;s time to add yet another item to the &&num;8220&semi;Is there anything Big Data can&&num;8217&semi;t do&quest;&&num;8221&semi; cheering section&period; Insurance fraud certainly doesn&&num;8217&semi;t sound very sexy&comma; but it&&num;8217&semi;s responsible in part for nasty things like premium increases&period; People who say that insurance fraud is a victimless crime are full of it&period; We&&num;8217&semi;re the ones who end up paying for those crimes&comma; where it hits us right in the bank account&period; Anything that can help battle insurance fraud is more than welcome&period;<&sol;p>&NewLine;<h2 style&equals;"text-align&colon; justify&semi;"><b style&equals;"line-height&colon; 1&period;5em&semi;">FNOL<&sol;b><&sol;h2>&NewLine;<p style&equals;"text-align&colon; justify&semi;">That&&num;8217&semi;s an acronym for First Notice Of Loss&period; People file a FNOL to start the process of making a claim&comma; basically informing their insurance company that they have sustained a loss that they intend to open up a claim for it&period; Typically&comma; this used to be done by a phone call&comma; but in the 21st century age of enhanced communication&comma; FNOLs wind up also coming in by email&comma; text&comma; SMS&comma; and even attached images&period;<&sol;p>&NewLine;<p style&equals;"text-align&colon; justify&semi;">With this barrage of information coming in&comma; as well as subsequent follow-up reports and addenda&comma; it&&num;8217&semi;s easy for a carrier to get swamped&comma; and therefore less able to spot inconsistencies&comma; duplicate claims&comma; or other less than proper practices&period; Thanks to Big Data&comma; that sheer volume of information can be managed&comma; and inaccuracies&comma; both accidental and deliberate&comma; can be caught&period;<&sol;p>&NewLine;<h2 style&equals;"text-align&colon; justify&semi;"><b>Not So Fast&comma; Naughty-spawn&excl;<&sol;b><&sol;h2>&NewLine;<p style&equals;"text-align&colon; justify&semi;">Big Data can head off a fraudulent claim simply by checking past customer information and comparing it to the current situation&period; Consider the following example&period; Someone files a claim citing the loss of personal property due to a house fire&period; But Big Data shows that similar claims were filed by other family members barely a year before&comma; and a social media history shows that the claimant actually gave away several items to another family member a few months before&period; With the information gathered through Big Data&comma; the fraud is caught before any claims get paid out&period;<&sol;p>&NewLine;<h2 style&equals;"text-align&colon; justify&semi;"><b>The Cure For False Medical Claims<&sol;b><&sol;h2>&NewLine;<p style&equals;"text-align&colon; justify&semi;">Here&&num;8217&semi;s an area that&&num;8217&semi;s ripe for abuse&comma; especially when it comes to fraudulent disability claims&period; Say someone puts in a claim for a back problem&comma; claiming that they were flat-out incapacitated and house-bound for eight weeks&period; And yet&comma; other information culled from Big Data shows purchases made by the claimant at a sporting-goods store&comma; reservations for two at a nearby ski lodge&comma; and the purchase of lift tickets and toboggan rentals&excl; Bringing all of this data together and spotting such discrepancies helps fake claims like this explode on the launch pad&period;<&sol;p>&NewLine;<p style&equals;"text-align&colon; justify&semi;">Less fraud in medical insurance means lower premiums and increased affordability&comma; something which is good news for the 15&percnt; of Americans who don&&num;8217&semi;t have health insurance&comma; a figure cited by the article &&num;8220&semi;Many Americans Still Lack Health Insurance&&num;8221&semi;&period;<&sol;p>&NewLine;<h2 style&equals;"text-align&colon; justify&semi;"><b>&&num;8220&semi;Do You Want To Change Your BS Story&comma; Sir&quest;&&num;8221&semi;<&sol;b><&sol;h2>&NewLine;<p style&equals;"text-align&colon; justify&semi;">Or perhaps someone puts in a claim for an auto accident&comma; citing loss of control of their vehicle in heavy traffic during bad weather&comma; resulting in the inability to stop in time&period; But information pulled from Big Data sources show that not only was the weather clear&comma; there was virtually no traffic on that roadway&comma; and&comma; most damning of all&comma; according to state records&comma; the car had failed inspection&comma; particularly due to&comma; yes&comma; bad brakes&period; The only way that could possibly be worse is if the driver had a history of drinking &&num;8230&semi; oh&comma; wait&comma; yes&comma; according to his driving record&comma; he&&num;8217&semi;s received several DUIs in the last few years&period; So much for that accident claim&period;<&sol;p>&NewLine;<p style&equals;"text-align&colon; justify&semi;">What the above shows us is that Big Data&comma; used in conjunction with analytics and data processing tools&comma; can gather vast amounts of information from disparate sources&comma; and assemble it into one very revealing&comma; very complete picture&period; What was once a herculean task can now be easily accomplished&comma; making it harder to perpetrate fraud&period;<&sol;p>&NewLine;<p style&equals;"text-align&colon; justify&semi;">So&comma; whether it&&num;8217&semi;s fighting fraud or helping you track down your lost dog&comma; yes&comma; it seems that there&&num;8217&semi;s nothing that Big Data can&&num;8217&semi;t do&period;<&excl;-- End Media Shower Content Reporting code --><&sol;p>&NewLine;

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