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.
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DutchMLSchool. ML Adoption
- 1. Jan W. Veldsink MSc 1rd edition | July 8 - 11, 2019
- 2. Jan W. Veldsink MSc THE ART OF AI AI - IN - A - CORPORATE Jan W Veldsink MSc
- 3. Jan W. Veldsink MSc
- 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. Jan W. Veldsink MSc Fraud Detection: Rules Since 2009 FUZZY LOGIC
- 6. Jan W. Veldsink MSc Event driven architecture
- 7. Jan W. Veldsink MSc
- 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. 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. Jan W. Veldsink MSc The organization
- 11. Jan W. Veldsink MSc What we have been using since 2008
- 12. Jan W. Veldsink MSc What we have been using 2018 -
- 13. Jan W. Veldsink MSc How to do ML in execution.. Model Logic / Rule / AI Engine Predictions
- 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. Jan W. Veldsink MSc Real implementation 2018
- 16. Jan W. Veldsink MSc In 2018 we started
- 17. Jan W. Veldsink MSc AI - Platform Roundtrip ML DataLake AI-ML-DataLake AI-Machine learning AI-ML-Actuation Alert and Case management
- 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. Jan W. Veldsink MSc Card_fraud
- 20. Jan W. Veldsink MSc Best Of Both worlds: Fraud Detection Candidate Fraudulent Transaction Validation of Fraudulent Transaction
- 21. Jan W. Veldsink MSc Topic-maps
- 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. 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. 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. Jan W. Veldsink MSc
- 26. Jan W. Veldsink MSc
- 27. Jan W. Veldsink MSc
- 28. Jan W. Veldsink MSc
- 29. Jan W. Veldsink MSc
- 30. Jan W. Veldsink MSc 8 building blocks
- 31. Jan W. Veldsink MSc How to do ML in teams.. ML/AI expert Data Labels Model
- 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. Jan W. Veldsink MSc AI is a business task ML/AI expert Domain Data expert Business Domain expert
- 34. Jan W. Veldsink MSc Data Scientist
- 35. Jan W. Veldsink MSc Decision engineering
- 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. 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. Jan W. Veldsink MSc
- 39. Jan W. Veldsink MSc RaboML
- 41. Jan W. Veldsink MSc 8 building blocks
- 42. Jan W. Veldsink MSc Design thinking
- 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. Jan W. Veldsink MSc ART OF AI Machine Learning and AI made beautifully simple