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Data Natives meets DataRobot | "Build and deploy an anti-money laundering model in 20 minutes" - Kayne Putman & Christian de Chenu

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Data Natives meets DataRobot | "Build and deploy an anti-money laundering model in 20 minutes" - Kayne Putman & Christian de Chenu

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Compliance departments within banks and other financial institutions are turning to machine learning for improving their Anti Money Laundering compliance activities. Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates. DataRobot will discuss how their Automated Machine Learning platform was successfully used for a real use case to reduce their false positives and to enhance their Anti-Money Laundering activities.

Compliance departments within banks and other financial institutions are turning to machine learning for improving their Anti Money Laundering compliance activities. Today, the systems that aim to detect potentially suspicious activity are commonly rule-based, and suffer from ultra-high false positive rates. DataRobot will discuss how their Automated Machine Learning platform was successfully used for a real use case to reduce their false positives and to enhance their Anti-Money Laundering activities.

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Data Natives meets DataRobot | "Build and deploy an anti-money laundering model in 20 minutes" - Kayne Putman & Christian de Chenu

  1. 1. Confidential. ©2019 DataRobot, Inc. – All rights reserved Kayne Putman Customer Facing Data Scientist DataRobot Build and deploy an Anti-Money Laundering model in 20 minutes. Christian de Chenu Customer Facing Data Scientist DataRobot
  2. 2. Confidential | Copyright © 2018 | DataRobot, Inc. Strawpoll ● Who has heard of (or is working with) DataRobot ● Who is working on an AML use case in their organisation?
  3. 3. Confidential. ©2019 DataRobot, Inc. – All rights reserved 1. Introduction to DataRobot (3 mins) 2. Introduction to AML (5 mins) 3. AML demo (15 mins) 4. Real AML use case (5 mins) 5. Q&A Agenda
  4. 4. Confidential. ©2019 DataRobot, Inc. – All rights reserved 1. Introduction to DataRobot 2. Introduction to AML 3. AML demo 4. Real AML use case 5. Q&A
  5. 5. Confidential. ©2019 DataRobot, Inc. – All rights reserved 2012 Invented Automated Machine Learning $224 M In funding to date 1.2B+/2.5M daily Models built on DataRobot in the cloud 800+ Worldwide employees 4 Major; 40+ Minor Product releases per year Thousands Successful AI Projects The World’s Most Trusted Automated Machine Learning Platform INSURANCE BANKING FINTECH HEALTHCARE TELECOM GOVERNMENT RETAIL MANUFACTURING MANY MORE
  6. 6. Automated Machine Learning: The benefits 2020 2022 2024 Increase Data Scientist Productivity Empower Data Analysts, Engineers, and other SMEs 2008 2010 2014 2016 2018 Companies are achieving AI success with automated machine learning AND the team they already have in place 2012 Demand for machine learning & AI Supply of data scientists
  7. 7. Confidential | Copyright © DataRobot, Inc. | All Rights Reserved ATTRIBUTES 1. Knowledge of the overall & specific missions 2. Knowledge of the data 3. Ability to write code to gather data 4. Ability to write code to explore/inspect data 5. Ability to write code to manipulate data 6. Ability to write code to extract actionable intel 7. Ability to write code to build models 8. Ability to write code to implement models 9. Foundational statistics 10. Internals of algorithms 11. Practical knowledge and experience 12. Knowing how to interpret and explain models DATAROBOT DEMOCRATIZES DATA SCIENCE Domain Expertise Math & Stats Domain Expertise Programming Skills
  8. 8. The AI-Driven Enterprise R&D Production Marketing Finance Sales HR C-Suite= AI system $$ $$$ $$$ $$$$$ $$ $ = Impact to the bottom line
  9. 9. Confidential | Copyright © 2018 | DataRobot, Inc. Building an AI-Driven Culture “AI will crush you!” OK, we’ll try a POC. Wow! $20M ROI from one use case! What else can we do? Great! Our Automation-first approach is delivering much more value to the business, and faster. Let’s empower and mentor more data-savvy talent. We’re changing the game in our industry. Our groups are working together on AI projects driving better outcomes. AI-Driven Enterprise Embed AI in all business processes Solution Acceleration Solve urgent business needs faster Data Scientist Productivity Get more projects done in less time Data Science Democratization Empower your existing team to build AI
  10. 10. Trusted AI Partner Every Step of the Way DataRobot University Provide practical, hands-on education for DataRobot users, from executives to analysts and data scientists, no matter what your data science experience may be 24/7 customer support Support customers through specialist knowledge that is equipped and empowered to fix any issue you may encounter, and ensure you're set up for success Account Executive Understand your objectives and recommend the best DataRobot resources Field Engineer Integrate DataRobot with customer infrastructure and production pipelines AI Success Manager Assess and coordinate resources needed to achieve ongoing success Customer Facing Data Scientist Deliver data science consulting to support use cases and enable value- producing users Account Team
  11. 11. Confidential. ©2019 DataRobot, Inc. – All rights reserved Technology Alliances Cloud RPA Analytics WorkbenchesSolutions Data & Hardware Platforms Amazon SageMaker
  12. 12. Confidential. ©2019 DataRobot, Inc. – All rights reserved 1. Introduction to DataRobot 2. Introduction to AML 3. AML demo 4. Real AML use case 5. Q&A
  13. 13. Confidential | Copyright © 2018 | DataRobot, Inc. Money Laundering (noun) Making funds obtained from illegal activity appear legitimate by concealing the source
  14. 14. Confidential | Copyright © 2018 | DataRobot, Inc. How does Money Laundering work?
  15. 15. Confidential | Copyright © 2018 | DataRobot, Inc. • Anti-Money Laundering (AML) Compliance Program pillars: ○ Know Your Customer (KYC) to establish customer identity and risk ○ Transaction Monitoring to detect potential money laundering activity → Suspicious Activity Reports (SAR) filed with the National Crime Agency in the UK. • Some recent penalties from the FCA (Financial Conduct Authority) for AML non-compliance: March 2019 £27.6 million March 2019 £34.3 million April 2019 £102.2 million How is Money Laundering prevented?
  16. 16. Confidential | Copyright © 2018 | DataRobot, Inc. Today, rule-based systems are used to refer potentially suspicious activity for manual review by internal investigators, which can result in a SAR. Transaction Data: Deposits, Payments, ... Transaction Monitoring Rules (can be billions of rows) No Alert Alert: Potentially Suspicious Expert Manual Review Not Suspicious Suspicious File SAR High False Positive Rate BEFORE: Rule-based transactional monitoring
  17. 17. Confidential | Copyright © 2018 | DataRobot, Inc. Alert: Potentially Suspicious Expert Manual Review Customer Order of Review SAR Filed Bob 1 No Barbara 2 No Walter 3 No Bill 4 No Bonny 5 No Wilma 6 No Barry 7 No Bart 8 No Brittany 9 No Willy 10 Yes Number of Manual Reviews False Positive Rate SARs Filed 10 1 90% BEFORE: Results
  18. 18. Confidential | Copyright © 2018 | DataRobot, Inc.
  19. 19. Confidential | Copyright © 2018 | DataRobot, Inc. False positive rates can be reduced using automated machine learning to yield a set of ranked alerts with a higher concentration of SARs. Transaction Monitoring Rules No Alert Alert: Potentially Suspicious Not Suspicious Suspicious File SAR Reduced False Positive Rate *NEW Full Manual Review of higher quality Alerts AFTER: Monitoring with Automated Machine Learning
  20. 20. Confidential | Copyright © 2018 | DataRobot, Inc. Alert: Potentially Suspicious Number of Full Manual Reviews False Positive Rate SARs Filed 1 4 90% 60% Full Manual Review of ranked, higher quality Alerts Time to find 10 SARs would drop from 30 hours to ~7.5 hours 10Customer Order of Review SAR Filed Bob 1 No Barbara 2 No Walter 3 No Bill 4 No Bonny 5 No Wilma 6 No Barry 7 Yes Bart 8 Yes Brittany 9 Yes Willy 10 Yes AFTER: Results
  21. 21. Confidential. ©2019 DataRobot, Inc. – All rights reserved 1. Introduction to DataRobot 2. Introduction to AML 3. AML demo 4. Real AML use case 5. Q&A
  22. 22. Confidential. ©2019 DataRobot, Inc. – All rights reserved 1. Introduction to DataRobot 2. Introduction to AML 3. AML demo 4. Real AML use case 5. Q&A
  23. 23. Confidential | Copyright © 2018 | DataRobot, Inc.Source: https://lemonadeday.org/blog/business-lessons-dilbert
  24. 24. Confidential | Copyright © 2018 | DataRobot, Inc. How to improve accuracy: iterate & evaluate
  25. 25. Confidential | Copyright © 2018 | DataRobot, Inc.
  26. 26. Confidential | Copyright © 2018 | DataRobot, Inc. Thought-provoker: In an AML use case, which feature(s) might be the most impactful?
  27. 27. Confidential | Copyright © 2018 | DataRobot, Inc.Source: http://www.scielo.org.za/scielo.php?script=sci_arttext&pid=S2224-78902017000200002
  28. 28. Confidential | Copyright © 2018 | DataRobot, Inc. Thank you. Questions?

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