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AI for Finance

I developed this presentation to discuss the framework for automation and autonomic operations in particular in the Finance domain. It is high level introductory but includes guidance of how to best select AI and RPA projects with higher implementation success rates. If you are interested in a copy dont be shy! Reach out!

I developed this presentation to discuss the framework for automation and autonomic operations in particular in the Finance domain. It is high level introductory but includes guidance of how to best select AI and RPA projects with higher implementation success rates. If you are interested in a copy dont be shy! Reach out!

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AI for Finance

  1. 1. AI for Finance September 11th, 2020. Dr. Kim Kyllesbech Larsen.
  2. 2. 2 Dr. Kim K. Larsen / How do we Humans feel about AI?
  3. 3. How do you feel about AI? 13% 50% 37% Negative Neutral Positive 10% of respondents are enthusiastic about AI. 11% are uncomfortable or scared about AI. Millennials are significantly more negative towards AI. Women tend to be less enthusiastic than men towards AI. SurveyMonkey surveys from August 2017 to August 2020. 2017 - 2020 2017 - 2020 Sentiment
  4. 4. Who we are serving Their experience matter!
  5. 5. 5 In the Future, do we need humans? 67% 33% No Yes 78% 22% No Yes Do you believe that your job could be replaced by an AI? Do you believe your colleagues jobs could be replaced by an AI?
  6. 6. Human decision making 17% 43% 40% Infrequently About half the time Frequently 52% 32% 16% Infrequently About half the time Frequently Would you trust a critical corporate decision made by a fellow human expert or superior? Would you trust a critical corporate decision made by an AI? AI decision makingvs
  7. 7. Automation vs autonomy Artificial Intelligence “Most organizations reported some failures among their AI projects with a quarter of them reporting up to 50% failure rate.” (IDC, July 2019). “Lack of skilled staff and unrealistic expectations were identified as the top reasons for failure.” (IDC, July 2019). “Robotic process automation failure rate is 30% - 50%” Ernst & Young. “only 3% of organizations were able to scale RPA to 50 or more bots” Deloitte UK.
  8. 8. RPA AI - “Classic” RPA. - Machine learning (ML) incl. deep learning (DL). - Natural Language Processing (NLP). - Natural Language Generation. - Computer vision (DL). - Intelligent Automation (RPA + AI). - … - Business Process Optimization (e.g., cost reduction). - Human complexity reduction (e.g., for complex processes). - (Tedious) manual labor reduction. - Customer Operations Processes & interactions. - Fraud / Anomaly detection. - Advanced business analytics. - Data driven decision making. - ….
  9. 9. ▪ Well defined or logical. ▪ Rule-based. ▪ Repetitive. ▪ Static or confined dynamical. ▪ Simple / simpler processes. ▪ Relative narrow processes. ▪ Simpler / narrower data context. ▪ Not exposed to biases & false outcomes. ▪ Infrastructure landscape fit, etc… When AI?
  10. 10. What AI? SPECIALISTIC (NARROW) AI ~100% of todays use cases Chat Bots
  11. 11. How AI? Need to “Clean” Data! Model / Architecture Quality Goals! (to train the model, normally not as heavy to run it) (need to beat “Flipping a Coin” or Majority “Vote”) It Starts Here! Train Test Computing power Lots of Data! (the more data, the higher quality should result & the less complexity required … in general)
  12. 12. 68% of your data often assumed to be The “Normal” Representing 95% of all your data Increasing risk of bias & neglect. Increasing risk of bias & neglect. “Anomalies” may hide here “Anomalies” may hide here AIs are very much tuned to where most data is available.
  13. 13. Regular pattern Anomaly AIs are very good at pattern recognition and modelling regularities as well as catching anomalies. Source: Anodot https://www.anodot.com/blog/what-is-anomaly-detection/ Anomaly detection should be a mandatory system component of any RPA, AI or Intelligent Automation implementation as for infrastructure & business process monitoring & operations.
  14. 14. The 3Z Ambition
  15. 15. Access Data Center = Cloud Experience n = n + 1 AI Controllers Learning Agents Environment Observations: Customer interactions. Actions Reward e.g., to achieve desired outcomes Many experience iterations per relevant time unit. 3Z Principles towards Intelligent Automation Services Customers ▪ Re-enforcement learning (ML/DL) ▪ Closing the loop. ▪ Dynamic machine learning. ▪ Anomaly detection on infrastructure as well as Learning Agents / RPAAs. “Closing the Loop”
  16. 16. Robotic Proces Automation No regret … if managed Industrial-scaled AI Higher complexity Chat bot(s) No regret … keep narrow Anomaly detection Essential & easy ROISpecialistic (narrow) AI Simpler tasks … easy ROI Intelligent Automation (beyond RPA or RPA meets AI) Higher complexity
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  18. 18. THANK YOU! Acknowledgement Many thanks many industry colleagues who have contributed with valuable insights, discussions & comments throughout this work. Also I would like to thank my wife Eva Varadi for her patience during this work. Contact: Email: kim.larsen@t-mobile.nl Linkedin: www.linkedin.com/in/kimklarsen Blogs: www.aistrategyblog.com & www.techneconomyblog.com Twitter: @KimKLarsen

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