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DutchMLSchool. ML Adoption

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Adopting Machine Learning at Scale - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.

Published in: Data & Analytics
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DutchMLSchool. ML Adoption

  1. 1. Jan W. Veldsink MSc 1rd edition | July 8 - 11, 2019
  2. 2. Jan W. Veldsink MSc THE ART OF AI AI - IN - A - CORPORATE Jan W Veldsink MSc

  3. 3. Jan W. Veldsink MSc
  4. 4. Jan W. Veldsink MSc Fraud Detection 5-25 Alerts are true hits 10.000.000 Amount of daily transactions 100 Daily alerts on fraud With analytics we are able to capture large portions of the fraudulent activities. RABOBANK
  5. 5. Jan W. Veldsink MSc Fraud Detection: Rules Since 2009 FUZZY LOGIC
  6. 6. Jan W. Veldsink MSc Event driven architecture
  7. 7. Jan W. Veldsink MSc
  8. 8. Jan W. Veldsink MSc The split Scoping Knowledge aquisition Creating Business Rules Data / Interface changes? Specify change in a RFC IT change Validate Business Rules Pas Business Rules toe Business Rules BusinessEngineeringICTconfigurationandchange Definine Business Rules Deliver change Testing changes Yes No Change done RFC
  9. 9. Jan W. Veldsink MSc Scoping Knowledge aquisition Creating Business Rules Data / Interface changes? Specify change in a RFC IT change Validate Business Rules Pas Business Rules toe Business Rules BusinessEngineeringICTconfigurationandchange Definine Business Rules Deliver change Testing changes Yes No Change done RFC The split Business IT
  10. 10. Jan W. Veldsink MSc The organization
  11. 11. Jan W. Veldsink MSc What we have been using since 2008
  12. 12. Jan W. Veldsink MSc What we have been using 2018 -
  13. 13. Jan W. Veldsink MSc How to do ML in execution.. Model Logic / Rule / AI Engine Predictions
  14. 14. Jan W. Veldsink MSc RiskShield – Overview // target RaboML RiskShield ML environment ▪ RiskShield Server comes with an in- built standardized Interface (PMML) supporting AI driven models ▪ The results of AI driven models will be stored to the database, leading to optimized models ▪ This process can be driven 24x7, periodically or event based ML DataLake
  15. 15. Jan W. Veldsink MSc Real implementation 2018
  16. 16. Jan W. Veldsink MSc In 2018 we started
  17. 17. Jan W. Veldsink MSc AI - Platform Roundtrip ML DataLake AI-ML-DataLake AI-Machine learning AI-ML-Actuation Alert and Case management
  18. 18. Jan W. Veldsink MSc Surveillance ▪ ▪ ▪ ▪ Debit/Credit card Trades Internet News Social Email Chat Documents Reports Reduced cost
 fraud,non-compliance, misconduct Detection through sophisticated scenarios Risk based prioritization of alerts and reduced false positives Transactions Communications External ▪ ▪▪▪ Surveillance ▪ Dataset = ORG Customers with only CDD=A Dataset = NP Customers with only CDD=A Split on NP - ORG Anomaly model per peergroup Age category _ Account type Filter anomalyscore > XX% Anomaly model per peergroup: SBI-2 code _ Account_type Filter anomalyscore > XX% OutputCreate explain clustering OutputCreate explain clustering
  19. 19. Jan W. Veldsink MSc Card_fraud
  20. 20. Jan W. Veldsink MSc Best Of Both worlds: Fraud Detection Candidate Fraudulent Transaction Validation of Fraudulent Transaction
  21. 21. Jan W. Veldsink MSc Topic-maps
  22. 22. Jan W. Veldsink MSc Monitoring and Predictive services Monitoring Fraud Anti money laundering Correspondent Banking Terrorism Financing Market abuse monitoring Some product rest risks CDD Customer Integrity Conflicts of interest Client Screening Sanctions
  23. 23. Jan W. Veldsink MSc Anomaly patterns / peer groups Dataset = ORG Customers with only CDD=A Dataset = NP Customers with only CDD=A Split on NP - ORG Anomaly model per peergroup Age category _ Account type Filter anomalyscore > XX% Anomaly model per peergroup: SBI-2 code _ Account_type Filter anomalyscore > XX% OutputCreate explain clustering OutputCreate explain clustering
  24. 24. Jan W. Veldsink MSc Event / Signal / ALERT / CASE Events Transactions Signals Alerts Cases Customer data Interesting events Inside and Just outside the thresholds Rules / Fuzzy logic / Scorecards / Dynamic profiling / Machine learning Rules / Fuzzy logic / Scorecards / Dynamic profiling / Machine learning Intelligent research / User assisted learning / Machine learning Alerted events Research Real cases AI AI AI
  25. 25. Jan W. Veldsink MSc
  26. 26. Jan W. Veldsink MSc
  27. 27. Jan W. Veldsink MSc
  28. 28. Jan W. Veldsink MSc
  29. 29. Jan W. Veldsink MSc
  30. 30. Jan W. Veldsink MSc 8 building blocks
  31. 31. Jan W. Veldsink MSc How to do ML in teams.. ML/AI expert Data Labels Model
  32. 32. Jan W. Veldsink MSc 4 T Model - Agile AI 4 t’s of AI Task Research AI Create AI Operationalize AI Team Diversity Power to execute Power to think Ability to think differently Trust Focus Mandate Technology AI ecosysteem Research tools Jan W. Veldsink MSc
  33. 33. Jan W. Veldsink MSc AI is a business task ML/AI expert Domain Data expert Business Domain expert
  34. 34. Jan W. Veldsink MSc Data Scientist
  35. 35. Jan W. Veldsink MSc Decision engineering
  36. 36. Jan W. Veldsink MSc Decision engineer in AI age • Decision intelligence is an engineering discipline that augments data science with theory from social science, decision theory, and managerial science. • Its application provides a framework for best practices in organizational decision-making and processes for applying machine learning at scale. https://en.wikipedia.org/wiki/Decision_Intelligence
  37. 37. Jan W. Veldsink MSc Place at RaboML Role Fte’s Lead 1 Team 2 Data-support 1 Virtual team 4 - 8 IT -support 0,25 Projected capacity 2019 A place to experiment and work ML/AI expert Domain Data expert Business Domain expert Data Labels Model A place to work on business projects AI(BigML)-Desk
  38. 38. Jan W. Veldsink MSc
  39. 39. Jan W. Veldsink MSc RaboML
  40. 40. Simple Emergentie/ TransitieComplicated Complex Unordered Ordered !40
  41. 41. Jan W. Veldsink MSc 8 building blocks
  42. 42. Jan W. Veldsink MSc Design thinking
  43. 43. Jan W. Veldsink MSc Key Take aways • Design an architecture for Machine learning and AI • Data -> ML -> Production • AI is a business task, design your organization to support this • Look at Decision Engineering as the task to be covered • Start experimenting • Keep experimenting • Involve as much business as possible • Educate and train staff and Senior managment • Building the right team!
  44. 44. Jan W. Veldsink MSc ART OF AI Machine Learning and AI made beautifully simple


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