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© 2015Hortonworks, Inc. All rights reserved. Hadoop and the Hadoop elephant logo are trademarks of the Apache Software Foundation. Claims leakage is a common challenge for the insurance industry. Today’s organizations use claims audit tools to optimize training, business rules and processes in an attempt to reduce leakage. But these are retroactive processes—they only catch leakage after the payment has been made. Moreover, it is not commercially viable to review every claim for leakage. Insurance companies seek competitive advantage by building capabilities to: 1) identify points in its process flow that are at greatest risk for claims leakage to occur, 2) streamline and systematize procedures and 3) to harness big data analytics for new applications. Today, claims leakage forensics occurs after the fact. By capturing big data and processing it with machine learning techniques, carriers can automate leakage analysis and apply it inline with claims processing to evaluate and optimize payouts. This helps minimize leakage by detecting it and correcting it promptly. Skytree Infinity™ is an enterpriseready Machine Learning (ML) platform designed from the ground up to efficiently work on massive and fast changing datasets. Skytree Infinity’s scalable architecture performs stateoftheart ML methods that were previously not possible on large data sets. The fundamental design of Skytree Infinity incorporates advanced algorithms from ML research to achieve speeds that are orders of magnitude faster than existing approaches. As a result, Skytree Infinity is ideal for complex analytics on big data such as claims leakage. Skytree Infinity is fully certified and runs natively on the latest version of Hortonworks Data Platform (HDP). It provides integrated Machine Learning capabilities via YARN and Spark—enabling customers to implement powerful endtoend analytic workflows from a single common platform. Minimizing Claims Leakage Using Machine Learning on Hadoop Use Case THE PROBLEM THE SOLUTION THE BENEFIT Insurance firms often overpay for claims. This is called claims leakage. Currently, determination of claims leakage is an expensive forensic process and leakage represents the largest expense item for insurance companies—often up to 70 or 80% of the operating cost. A combination of Apache Hadoop and machine learning is required to store and analyze patterns in existing claims and available forensic data. The resulting models can then incorporate the learning inline with new claims processing and optimize payouts. With machine learning on big data, insurance firms pull forensic insights upstream and insert them inline with claims investigation, evaluation and settlement. In this way, they achieve big savings by reducing operating costs and claims overpayments. Partner Brief www.hortonworks.com “With the exponential growth of Big Data, Machine Learning is a necessary tool to manage advanced analytics. We believe that the combination of Skytree Infinity with Hortonworks Data Platform will allow customers to get more business value from their existing infrastructures with predictive analytics over largescale data sets.” Dylan Steeg Vice President, Business Development Skytree, Inc.

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Page 1: MinimizingClaims LeakageUsing MachineLearning …docs.media.bitpipe.com/io_12x/io_123029/item_1150690/...©2015Hortonworks,"Inc."All"rights"reserved."Hadoop"and"the"Hadoop"elephantlogo"are"trademarks"of"the"Apache"Software"Foundation."!

©  2015Hortonworks,  Inc.  All  rights  reserved.  Hadoop  and  the  Hadoop  elephant  logo  are  trademarks  of  the  Apache  Software  Foundation.    

Claims  leakage  is  a  common  challenge  for  the  insurance  industry.  Today’s  organizations  use  claims  audit  tools  to  optimize  training,  business  rules  and  processes  in  an  attempt  to  reduce  leakage.  But  these  are  retroactive  processes—they  only  catch  leakage  after  the  payment  has  been  made.  Moreover,  it  is  not  commercially  viable  to  review  every  claim  for  leakage.    

Insurance  companies  seek  competitive  advantage  by  building  capabilities  to:  1)  identify  points  in  its  process  flow  that  are  at  greatest  risk  for  claims  leakage  to  occur,  2)  streamline  and  systematize  procedures  and  3)  to  harness  big  data  analytics  for  new  applications.  Today,  claims  leakage  forensics  occurs  after  the  fact.  By  capturing  big  data  and  processing  it  with  machine  learning  techniques,  carriers  can  automate  leakage  analysis  and  apply  it  in-­‐line  with  claims  processing  to  evaluate  and  optimize  payouts.  This  helps  minimize  leakage  by  detecting  it  and  correcting  it  promptly.  

Skytree  Infinity™  is  an  enterprise-­‐ready  Machine  Learning  (ML)  platform  designed  from  the  ground  up  to  efficiently  work  on  massive  and  fast  changing  datasets.  Skytree  Infinity’s  scalable  architecture  performs  state-­‐of-­‐the-­‐art  ML  methods  that  were  previously  not  possible  on  large  data  sets.  The  fundamental  design  of  Skytree  Infinity  incorporates  advanced  algorithms  from  ML  research  to  achieve  speeds  that  are  orders  of  magnitude  faster  than  existing  approaches.  As  a  result,  Skytree  Infinity  is  ideal  for  complex  analytics  on  big  data  such  as  claims  leakage.    

Skytree  Infinity  is  fully  certified  and  runs  natively  on  the  latest  version  of  Hortonworks  Data  Platform  (HDP).  It  provides  integrated  Machine  Learning  capabilities  via  YARN  and  Spark—enabling  customers  to  implement  powerful  end-­‐to-­‐end  analytic  workflows  from  a  single  common  platform.  

 

Minimizing  Claims  Leakage  Using  Machine  Learning  on  Hadoop  

   

Use  Case  

THE  PROBLEM     THE  SOLUTION     THE  BENEFIT  Insurance  firms  often  overpay  for  claims.  This  is  called  claims  leakage.  Currently,  determination  of  claims  leakage  is  an  expensive  forensic  process  and  leakage  represents  the  largest  expense  item  for  insurance  companies—often  up  to  70  or  80%  of  the  operating  cost.  

  A  combination  of  Apache  Hadoop  and  machine  learning  is  required  to  store  and  analyze  patterns  in  existing  claims  and  available  forensic  data.  The  resulting  models  can  then  incorporate  the  learning  in-­‐line  with  new  claims  processing  and  optimize  payouts.  

  With  machine  learning  on  big  data,  insurance  firms  pull  forensic  insights  upstream  and  insert  them  in-­‐line  with  claims  investigation,  evaluation  and  settlement.  In  this  way,  they  achieve  big  savings  by  reducing  operating  costs  and  claims  overpayments.  

   

Partner  Brief  

www.hortonworks.com  

“With  the  exponential  growth  of  Big  Data,  Machine  Learning  is  a  necessary  tool  to  manage  advanced  analytics.  We  believe  that  the  combination  of  Skytree  Infinity  with  Hortonworks  Data  Platform  will  allow  customers  to  get  more  business  value  from  their  existing  infrastructures  with  predictive  analytics  over  large-­‐scale  data  sets.”  

Dylan  Steeg  Vice  President,  Business  Development  Skytree,  Inc.  

 

 

Page 2: MinimizingClaims LeakageUsing MachineLearning …docs.media.bitpipe.com/io_12x/io_123029/item_1150690/...©2015Hortonworks,"Inc."All"rights"reserved."Hadoop"and"the"Hadoop"elephantlogo"are"trademarks"of"the"Apache"Software"Foundation."!

©  2015Hortonworks,  Inc.  All  rights  reserved.  Hadoop  and  the  Hadoop  elephant  logo  are  trademarks  of  the  Apache  Software  Foundation.    

Skytree  in  the  Modern  Data  Architecture  

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For  additional  questions,  contact:      • Skytree,  Inc.  

www.skytree.net                                                  (408)  392-­‐9300  

• Hortonworks  www.hortonworks.com  (855)  8-­‐HORTON  

Hortonworks  is  a  leading  commercial  vendor  of  Apache  Hadoop,  the  open  source  platform  for  storing,  managing  and  analyzing  Big  Data.  Hortonworks  Data  Platform,  our  distribution  of  Apache  Hadoop,  provides  an  open  and  stable  foundation  for  enterprises  and    a  growing  ecosystem  to  build  and  deploy  Big    Data  solutions.    

Hortonworks  is  the  trusted  source  for  information  on  Hadoop,  and  together  with  the  Apache  community,  Hortonworks  is  making  Hadoop  an  enterprise    data  platform.  Hortonworks  provides  unmatched  technical  support,  training  and  certification  programs  for  enterprises,  systems  integrators  and    technology  vendors.    

Hortonworks.  We  do  Hadoop.  

 

FEATURES  &  BENEFITS  OF  COMBINED  SOLUTION  

• High-­‐speed  analysis  of  information  collected  in  Hortonworks  Data  Platform  (HDP)  at  scale  

• Support  for  a  wide  variety  of  data  formats  and  sources  • Integrated  and  tested  support  for  HDP  as  part  of  the  

Hortonworks  Technology  Certified  partner  program  • A  comprehensive  solution  that  supports  a  variety  of  

analytic  methods  and  algorithms  to  provide  predictive  analysis  with  Skytree  Infinity  

• Close  integration  of  Skytree  Infinity  with  HDP  for  seamless  deployment  in  existing  Hadoop  clusters,  without  the  need  to  move  data      

 

 

Skytree®—The  leader  in  enterprise  machine  learning  on  big  data,  is  disrupting  the  advanced  analytics  market  with  a  machine  learning  platform  that  gives  organizations  the  power  to  discover  deep  analytic  insights,  predict  future  trends,  make  recommendations  and  reveal  untapped  markets  and  customers.  Skytree’s  flagship  product—Skytree  Infinity™—is  the  only  general-­‐purpose  platform  on  the  market,  built  for  the  highest  accuracy,  speed  and  scalability.  

 

Skytree  is  a  Certified  Hortonworks  Technology  Partner  The  Hortonworks  Certified  Technology  Program  reviews  and  certifies  technologies  for    architectural  best  practices,  validated  against  a  comprehensive  suite  of  integration  test  cases,  benchmarked  for  scale  under  varied  workloads  and  comprehensively  documented.