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Paulo Gottgtroy - Machine Learning and Deep Learning – Creating the Perfect Storm

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Paulo Gottgtroy, Chief Data Scientist - Inland Revenue NZ : Speaking during the Chief Data & Analytics Officer New Zealand- November 2017, by Corinium Intelligence. Visit: http://chiefdataanalyticsofficernz.com/ for more information

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Paulo Gottgtroy - Machine Learning and Deep Learning – Creating the Perfect Storm

  1. 1. Machine Learning and Deep Learning Creating the “Perfect” Storm
  2. 2. AI winter
  3. 3. AI winter Marvin Lee Minsky (August 9, 1927 – January 24, 2016) was an American cognitive scientist concerned largely with research of artificial intelligence (AI), co-founder of the Massachusetts Institute of Technology's AI laboratory, and author of several texts concerning AI and philosophy. Seymour Aubrey Papert (/ˈpæpərt/; February 29, 1928 – July 31, 2016) was a South African-born American mathematician, computer scientist, and educator, who spent most of his career teaching and researching at MIT.[2][3][4] He was one of the pioneers of artificial intelligence, and of the constructionist movement in education.[5] He was co-inventor, with Wally Feurzeig and Cynthia Solomon, of the Logo programming language.
  4. 4. “Marketing organizations do need a team of analytics professionals who understand data and the technologies that integrate it. But beyond that, executives should place more emphasis on data science than on data scientists. “
  5. 5. Data Science Role The new role analytics play in supporting enterprise-wide decision making Value Add, not Operation cost Business value assessment before investment cost estimation Risk Understanding Manage uncertainty by optimizing decisions Continuous Improvement Innovation and agility based on a holistic view of the enterprise strategies
  6. 6. Decision Management “improve the value created through each decision by managing the trade-offs between precision or accuracy, … agility, speed … within organizations” Experimental Enterprise “a deep-rooted approach to operations, innovation and competition … cheap, quick experimentation … mustn’t break the important production processes …” Lean Analytics “a start-up cycle of learning and adapting that’s driven by data … analytics, done right: lean, mean, and iteratively” Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics (IBM Press) Lean Analytics: Use Data to Build a Better Startup Faster (Lean Series)
  7. 7. Business Strategies Business Value Business Objectives Requirements Data Use Cases Collaboration Workloads
  8. 8. Over the last 6 years on-time payments have increased by 1%, from 83 to 84 per cent, over the same time on-time filing of tax returns has moved from 60% to 76%. The question is what is required to lift on-time payment rates so there are lessor numbers of debtors to follow up on and Government receives more cash faster ? Over this same period Inland Revenue has become quite sophisticated in operating campaigns and growing our campaign management capability, even with this, little movement has been achieved in the on-time payment rates. The current working hypothesis is that this result is directly related to available cash flow within the whole system of business and individuals lives that makes it difficult for this group of customers to pay on time. Note there is evidence that this group has a mix of new and repeat customers in it. Different information is required to understand more about the group of customers who are either regularly late with their payments or miss on a number of occasions over time . Current Constraints: Segmentation, intervention and campaign design is limited by the current data sets we have available and our ability to “test run” scenarios in a sand pit type environment . While the issue can be dealt with through “normal campaign management techniques” of build, run, test and learn, the current effort required to maintain today’s range of campaigns does not easily allow for this to happen. In other words we’d have to stop some of the current campaign effort and redirect it to these new actions. The risk of doing this is on-time payments would reduce, due to ineffective “new” campaigns being run, meaning additional work to be completed to get those customers back on track. Future State Options: One can imagine a future where through the use of “data lake type capabilities” we are able to enhance the effectiveness of campaigns “on the run” as customers behaviours change in response to the interventions designed and implemented across the spectrum of Prevent, Assist, Recover and Enforce.
  9. 9. Over the last 6 years on-time payments have increased by 1%, from 83 to 84 per cent, over the same time on-time filing of tax returns has moved from 60% to 76%. The question is what is required to lift on-time payment rates so there are lessor numbers of debtors to follow up on and Government receives more cash faster ? Over this same period Inland Revenue has become quite sophisticated in operating campaigns and growing our campaign management capability, even with this, little movement has been achieved in the on-time payment rates. The current working hypothesis is that this result is directly related to available cash flow within the whole system of business and individuals lives that makes it difficult for this group of customers to pay on time. Note there is evidence that this group has a mix of new and repeat customers in it. Different information is required to understand more about the group of customers who are either regularly late with their payments or miss on a number of occasions over time . Current Constraints: Segmentation, intervention and campaign design is limited by the current data sets we have available and our ability to “test run” scenarios in a sand pit type environment . While the issue can be dealt with through “normal campaign management techniques” of build, run, test and learn, the current effort required to maintain today’s range of campaigns does not easily allow for this to happen. In other words we’d have to stop some of the current campaign effort and redirect it to these new actions. The risk of doing this is on-time payments would reduce, due to ineffective “new” campaigns being run, meaning additional work to be completed to get those customers back on track. Future State Options: One can imagine a future where through the use of “data lake type capabilities” we are able to enhance the effectiveness of campaigns “on the run” as customers behaviours change in response to the interventions designed and implemented across the spectrum of Prevent, Assist, Recover and Enforce. “We are able to enhance the effectiveness of campaigns “on the run” as customers behaviours change in response to the interventions” David Udy Collections GM
  10. 10. Operating Strategies  Customer Centric  Intelligence Led  Agile  Networked Org. Use Cases  Asset Management  Property Assets  Corporate behaviour  Companies ownership  Risk Management  New Debtors vs Repetitive Debtors  Organised Crime Analysis  Dark Network connection Business Value  More cash faster  Less Number of debtors  Better management of customer intelligence  Campaign Automation  Self-service analysis Workloads  Data Preparation  Companies office  Property  Knowledge Graph  Property  Dark Network  Companies Office  Modelling  Phoenix  Income Suppression  Debtor behaviour Prediction Identify Gaps  Data  System  Technology  Smart Data Lake Business Objectives  Increase the number of on-time payments  Better collection strategies for “new” debtors Key Metrics  On-time payment increase  Campaign return on investment
  11. 11. The “Perfect” Storm
  12. 12. 1982
  13. 13. Convergence
  14. 14. Artificial Augmented

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