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[Ai in finance] AI in regulatory compliance, risk management, and auditing

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AI to Improve Regulatory Compliance, Governance & Auditing. How AI identifies and prevents risks, above and beyond traditional methods. Techniques and analytics that protect customers and firms from cyber-attacks and fraud. Using AI to quickly and efficiently provide evidence for auditing requests.

Published in: Economy & Finance
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[Ai in finance] AI in regulatory compliance, risk management, and auditing

  1. 1. AI in Finance: Regulatory Compliance, Risk Management, and Auditing Natalino Busa - Head of Data Science https://en.wikipedia.org/wiki/File:DTI-sagittal-fibers.jpg
  2. 2. 2 Head of Applied Data Science at Teradata On most networks: @natbusa
  3. 3. 3 What’s in the next hour? Machine learning and cognitive computing for : • Regulatory Compliance • Process and Financial Audit • Operational Risk Management • Data Management Discussion • Q&A • Ideas, use cases Recommendations • Data computing systems • Tools, and Skills
  4. 4. 4 A brief history of Regulatory Compliance
  5. 5. 5http://www.bibler.org/images/overlay/reni-moses_with_the_tables_of_the_law.jpg
  6. 6. 6 It does not scale well ...
  7. 7. 7 Moving towards digital Design QueriesVerify? Comply? Run Queries Check Reporting
  8. 8. 8 Meet the band at the bank Business developer Data Scientist Database Admin Devops
  9. 9. 9 Approach #1: avoid risk
  10. 10. 10 Approach #2: Accept Risk
  11. 11. 11 Approach #3: Data-Driven Regulatory Compliance
  12. 12. 12 Data-driven Compliance ❏ Distributed systems: time ❏ Complete, Correct, Coherent Information ❏ Human and Machine Errors ❏ Incorrect Interpretation of Regulations ❏ Attacks, Frauds, Exploits
  13. 13. 13 Auditing + Compliance + ORM Counting Things Known Knowns Talking about the: Expressive Power of SQL
  14. 14. 14 AI and Machine Learning to the rescue NLP - Process Mining Anomaly Detection - Knowledge Graph
  15. 15. 15 What is NLP? Transforming free text into structured data and back Computer Science + Linguistic Based on Algorithms, Machine Learning and Statistics
  16. 16. 16 What problems does NLP solve? Question Answering Machine Translation Text Summarization Topic Extraction Semantic Search Spelling correction Sentiment Analysis
  17. 17. 17 LDA: extract topics from documents
  18. 18. 18 Semantic Search The ... sleeps on the mat w(t-1) w(t+1) w(t+2) w(t+3) w(t+4) embedding w(t) dog, cat
  19. 19. 19 Semantic Search Word2Vec, Par2Vec, Doc2Vec https://arxiv.org/pdf/1405.4053v2.pdf https://arxiv.org/pdf/1301.3781v3.pdf
  20. 20. 20 NLP to SQL Transform Natural Language Question to Database SQL queries
  21. 21. 21 Google: open source NLP parser scoring 95% in grammar accuracy https://github.com/tensorflow/models/tree/master/syntaxnet
  22. 22. 22 Deep Learning in Language Parsing
  23. 23. 23 Operational Risk Management - Benefits - 1. Reduction of operational loss. 2. Lower compliance/auditing costs. 3. Early detection of unlawful activities. 4. Reduced exposure to future risks. Establish Context Risk assessment Identification Analysis - Evaluation Risk Treatment Monitor & Review ORM
  24. 24. 24 Unknown unknowns in ORM Looking for something we don’t know we should be looking for • Unexpected Services Failing • Suspicious Access schemes • Fraudulent Transactions Looking for Patterns, Looking for Anomalies IT Risk Model Risk Legal Risk CyberSecurity Fraud Risk Conduct Risk Money Laundry
  25. 25. 25 Network Traffic Patterns Classification
  26. 26. 26 Network Intrusion detection http://billsdata.net/?p=105 It contains 130 million flow records involving 12,027 distinct computers over 36 days (not the full 58 days claimed for the entire data release). Each record consists of: time (to nearest second), duration, source and destination computer ids, source and destination ports, protocol, number of packets and number of bytes Techniques: TDA, Dimensionality Reduction https://en.wikipedia.org/wiki/Nonlinear_dimensionality_reduction
  27. 27. 27 AI + Human Intelligence - It’s a closed loop -
  28. 28. 28 AI Tools http://www.shivonzilis.com/machineintelligence https://www.oreilly.com/ideas/the-current-state-of-machine-intelligence-3- 0
  29. 29. 29 AI based solutions: Enterprises http://www.shivonzilis.com/machineintelligence https://www.oreilly.com/ideas/the-current-state-of-machine-intelligence-3- 0
  30. 30. 30 AI based solutions: Industries http://www.shivonzilis.com/machineintelligence https://www.oreilly.com/ideas/the-current-state-of-machine-intelligence-3- 0
  31. 31. 31 AI based solutions AI Tools: Exist but may be too narrow in scope Data + Libraries + Scientists: Still the way to go for customization
  32. 32. 32 Thank you. natbusa
  33. 33. 33 bonus slides
  34. 34. 34 Hype it right. Understand the limitations of current systems Google’s Self-Driving Car Program Odometer Reaches 2 Million Miles http://www.wsj.com/articles/googles-self-driving-car-program-odometer-reaches-2-million-miles-1475683321 Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis http://www.nature.com/articles/srep26286 Why is AI so difficult? http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html http://www.forbes.com/sites/gilpress/2016/10/31/12-observations-about-artificial-intelligence-from-the-oreilly-ai-conference/ http://www.tor.com/2011/06/21/norvig-vs-chomsky-and-the-fight-for-the-future-of-ai/ https://www.safaribooksonline.com/library/view/oreilly-ai-conference/9781491973912/video260721.html Down the rabbit hole. https://youtu.be/_1Cyyt-4-n8 https://youtu.be/u6aEYuemt0M https://youtu.be/bEUX_56Lojc

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