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Big data & analytics for banking new york lars hamberg

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BIG DATA & ANALYTICS FOR BANKING SUMMIT, New York, 1 Dec 2015.
Keynote address: "How Predictive Analytics will change the Financial Services Sector”
Speaker : Lars Hamberg
http://www.specialistspeakers.com/?p=8367
Overview & Outlook: Why Big Data will over-deliver on its hype and transform Financial Services; Use cases with Advanced Analytics and Big Data Analytics in Financial Services, in Production & Distribution of banking products; new opportunities for incumbents in tomorrow’s ecosystem; big data, bigdata, analytics, smart data, data analytics, digitization, digitalization, predictive analytics, sentiment analysis, financial services, banking, asset management, distribution, retail, trading, technology, innovation, fintech, wealth, asset management, investment industry, robo advisory, social investing, behavior, profiling, client segmentation, alias matching, semantic memory models, unstructured data, machine learning, pattern recognition

Publicado en: Tecnología
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Big data & analytics for banking new york lars hamberg

  1. 1. Lars Hamberg Predictive Analytics in The Investment Industry   BIG DATA & ANALYTICS FOR BANKING SUMMIT December 1 & 2, 2015 | New York
  2. 2. STRATEGIC SCOPE OVERVIEW & OUTLOOK  
  3. 3. QUESTION: Has Big Data failed  
  4. 4. QUESTION 1: Has Big Data failed …to deliver on its hype?  
  5. 5.   Big  data  defini'on?  
  6. 6.   Big  data  defini'on?   “too  large  OR  complex     for  tradi'onal  processing  methods”  
  7. 7.        general blur ? buzzwords and distinction?   big  data  analy'cs     or     advanced  analy'cs   ?   predic've   analy'cs     or     big  data   predic've   analy'cs                             ?     machine  learning  ?      
  8. 8.    modern companies want to know everything – what about banks?   structured  data       advanced  analy'cs       staying  ahead  of   the  connected   clients         predic've   analy'cs                                   machine  learning        
  9. 9. QUESTION 2: Why has Big Data failed to deliver on its hype?  
  10. 10. B I G D A T A – V I S I O N R U N N I N GA H E A D O F R E A L I T Y ?
  11. 11. BIG DATA HYPE CYCLE Trigger Enlightenment Plateau of Productivity Peak of Inflated Expectations Trough of Disillusion
  12. 12. BIG DATA HYPE CYCLE Trigger Enlightenment Plateau of Productivity Peak of Inflated Expectations Trough of Disillusion
  13. 13. BIG DATA HYPE CYCLE WHY ARE WE HERE? Trough of Disillusion
  14. 14. BIG DATA HYPE CYCLE THE CHALLENGE OF DEALING WITH THE REALITY OF THE BIG UNSTRUCTURED DATA STACK Trough of Disillusion
  15. 15. In the big data stack Unstructured 80-90%
  16. 16. 2.5+  million   words  in  english    
  17. 17. QUESTION 3: Will Big Data deliver on its hype?  
  18. 18. Yes!  
  19. 19. Why?
  20. 20. Confluence of trends      
  21. 21. Confluence of trends  1. Data explosion - 24m doubling rate    
  22. 22. Confluence of trends  1. Data explosion - 24m doubling rate 2. Smartphone user behavior – 150x/day  
  23. 23. Confluence of trends  1. Data explosion - 24m doubling rate 2. Smartphone user behavior – 150x/day 3. Breakthrough technologies: Machines are learning to ”read and understand” unstructured data on a large scale
  24. 24. Confluence of trends  1. Data explosion - 24m doubling rate 2. Smartphone user behavior – 150x/day 3. Breakthrough technologies: Machines are learning to ”read and understand” unstructured data on a large scale (!)
  25. 25. Confluence of trends  = the necessary conditions for Big Data to deliver - and even ”over-deliver” - on its hype and to transform many industries, including the Investment Industry
  26. 26. Breakthroughs  
  27. 27. Understanding   the  meaning     of  words…  
  28. 28. Big  Data  Predic5ve   Analy5cs  in  Produc5on   and  in  Distribu5on  
  29. 29. Big  Data  Predic5ve  Analy5cs  
  30. 30. Magic  or  OSINT?  
  31. 31.      Magic  or   OSINT?  
  32. 32. Big  Data  Predic5ve   Analy5cs  in  Produc5on   and  in  Distribu5on  
  33. 33. The mysteries of monetizing vast streams of unstructured language data… From data… …to dollars?
  34. 34. MUCH BETTER TEXT ANALYTICS MUCH BETTER PREDICTIONS MUCH BETTER SENTIMENTS
  35. 35. Magic  or  OSINT?  wha5smonitor.com  
  36. 36. Big  Data  Predic5ve   Analy5cs  in  Produc5on   and  in  Distribu5on  
  37. 37. I  am  special,  but  not  very  different…    
  38. 38. Watson  Personality  Profile  (IBM)   Seman'c  Profile  (Gavagai)  
  39. 39. Use cases from financial industry   Distribution of investment products with massive increases in profitability – Task & Question Common denominators for success Common denominators for failure
  40. 40. Use cases from financial industry   Production of investment products – alpha creation with big data predictive analytics – Task & Question Common denominators for success Common denominators for failure
  41. 41. QUESTION 4: Impact of Big Data on your business?  
  42. 42. Huge (transformative) Imminent (surprising) Winners & Losers (during the shift – like in all major shifts)  
  43. 43. Key take-aways: Big Data has so far failed to deliver on its promise/hype – BUT will over-deliver on its hype: Unsupervised, learning, scalable, systems that understand the Big Data stack of language data, enabling useful sentiment analysis and useful prediction analytics on vast amounts of data Predictive analytics, profiling, alias matching – use cases show huge potential for disrupting most industries…Expect a transformative shift in competitive landscape across most industries – including banking Banks – expect margin pressure, use case success stories in analytics & opportunities for incumbents in and outside “non- bank services” – uniquely positioned as distribution partners Everybody can be a winner in this imminent shift – get involved in advanced data analytics!
  44. 44. Lars Hamberg Predictive Analytics in The Investment Industry   BIG DATA & ANALYTICS FOR BANKING SUMMIT December 1 & 2, 2015 | New York

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