Difference between revisions of "Risk, Compliance and Regulation"
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| − | <b> | + | <b>Getting Started with Machine Learning in Risk Management |
| − | </b><br> | + | </b><br>This week on Office Hours (https://officehou.rs), our OG hosts Dan Zitting and Kevin Legere are back and are ready to talk machine learning. We get a lot of questions about where to start with machine learning... lots of people in audit, risk, compliance, etc. are talking about it in principle but very few have started on a single practical use. In this episode, Kevin will show you where he actually got started using fancy words like k-means clustering to put machine learning to work in his actual job. Also, his Blue Jays hat in the shot is really gross (no one likes the Blue Jays) but Dan looks like he started combing his hair into a combover. Enjoy! ABOUT SERIES Office Hours is a work of passion to share strategies, technology ideas, and real-world stories that inspire governance, risk management, compliance, and audit professionals to live their biggest impact! Our channel is dedicated to delivering the best stories and strategies in developing GRC programs we've seen across 7,000 organizations in 140 countries around the world. In every episode we'll drill down on a topic that can help you level up - risk management, compliance automation, data analytics, next-generation auditing, robotic process automation, artificial intelligence, etc. |
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| − | <b> | + | <b>Artificial Intelligence (AI) in risk management - [[IBM]], 4th-IR, [[Microsoft]] & [[Google]] |
| − | </b><br> | + | </b><br>Imperial College Business School, Trestle Group and the IRM gathered experts from [[IBM]], 4th-IR, [[Microsoft]], and [[Google]]. - Bernhard Janischowsky (Trestle) - 00:00:05 - Tarun Ramadorai (Imperial) - 00:01:36 - Nicola Crawford (IRM Chair) - 00:04:25 - Raza Sadiq (IRM SIG Chair) - 00:05:47 - Grace Brasington ([[IBM]]) - 00:06:50 - Frank Luijckx (4th-IR) - 00:17:45 - Martin Moeller ([[Microsoft]]) - 00:26:26 - Matt McNeill ([[Google]]) - 00:33:43 - Panel discussion - 00:45:50 |
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| − | <b> | + | <b>Machine Learning A revolution in risk management and compliance |
| − | </b><br> | + | </b><br>Training on Machine Learning A revolution in risk management and compliance for FRM part 2 Current Issues including topics like Process of Machine Learning, Machine learning approaches, Application of machine learning approaches within the financial services sector, Application of machine learning in Credit risk and revenue modelling, Application of machine learning in Fraud |
| + | Application of machine learning in Surveillance of conduct and market abuse in trading by Vamsidhar Ambatipudi | ||
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| − | <b> | + | <b>97 How might artificial intelligence affect risk management? |
| − | </b><br> | + | </b><br>There's a lot of talk about artificial intelligence (AI) these days, and how it might change the world in the near future. Can AI help us manage risk better? David's answer is Yes and No! |
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| − | <b> | + | <b>AI Governance & Risk Management | Kartik Hosanagar | Talks at [[Google]] |
| − | </b><br> | + | </b><br>Join Talks at [[Google]] for a conversation with Kartik Hosanagar, John C. Hower Professor of Technology and Digital Business at Wharton, about his new book A Human’s Guide to Machine Intelligence. The book is the result of years of Professor Hosanagar’s research, and explores the impact of algorithmic decisions on our personal and professional lives, and their unanticipated consequences. Kartik will explore how firms can make use of the tremendous opportunities and potential offered by machine learning and automated decision-making, while also doing their part to ensure algorithms are responsibly deployed. About Kartik: Kartik Hosanagar is the John C. Hower Professor of Technology and Digital Business at the University of Pennsylvania’s Wharton School of Business. Professor Hosanagar’s research focuses on the digital economy, in particular the impact of analytics and algorithms on consumers and society, Internet media, Internet marketing and e-commerce. Kartik has been recognized as one of the world’s top 40 business professors under 40. |
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| − | <b> | + | <b>AWS re:Inforce 2019: Leadership Session: Governance, Risk, and Compliance (GRC326-L) |
| − | </b><br> | + | </b><br>Vice President of Security Chad Woolf, Director of Global Security Practice Hart Rossman, and Security Engineer Rima Tanash explain how governance functionality can help ensure consistency in your compliance program. Some specific services covered are [[Amazon]] GuardDuty, AWS Config, AWS CloudTrail, [[Amazon]] CloudWatch, [[Amazon]] Macie, and AWS Security Hub. The speakers also discuss how customers leverage these services in conjunction with each other. Additional attention is paid to the concept of ""elevated assurance,"" including how it may transform the audit industry going forward. Finally, the speakers discuss how AWS secures its own environment, as well as talk about the control frameworks of specific compliance regulations. |
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| − | <b> | + | <b>Transforming Compliance With Artificial Intelligence |
| − | </b><br> | + | </b><br>Learn and grow with more BrightTALK webinars and Talks on FinTech Security right here: http://bit.ly/2q4NciN Join our live webinar to learn which elements of AI are driving value for risk and compliance? (i.e. ML, NLP, RPA). Also learn about AI in practice - how regulated financial institutions are utilizing AI today, why why AI is the only answer to addressing big data compliance challenges, at scale. We will also go over what happened when RegTech met SupTech – how are regulators are applying AI to ease and accelerate compliance processes. |
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| − | <b> | + | <b>Compliance.ai Introduction |
| − | </b><br> | + | </b><br>Compliance.ai provides focused regulatory insight. Saving time, reducing cost & risk, while giving compliance officers much needed peace of mind. |
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| − | <b> | + | <b>AI augmenting compliance processes |
| − | </b><br> | + | </b><br>Jorg Schaper, Global Head of Proposition, KYC & Future Screening Platform, Thomson Reuters, talks about the main challenges organisations are facing with compliance screening, the impact of artificial intelligence and machine learning on KYC and AML compliance processes and how regulation and technology will evolve in the coming years. |
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| − | <b> | + | <b>Machine learning: The future of compliance? - Sibos 2016 |
| − | </b><br> | + | </b><br>Machine Learning or Artificial Intelligence is gaining greater public and business awareness. From self-driving cars to cancer research, the long-predicted benefits are becoming reality. Machine Learning’s core characteristics – self-learning, intelligent yet consistent decision-making and managing complexity – are all attractive from a compliance perspective. What new technologies and approaches might support compliance programmes in the (near) future? Is the regulatory environment suitable for their adoption? How can the industry fully leverage the potential benefits? |
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Revision as of 15:48, 7 September 2020
Youtube search... ...Google search
- Case Studies
- AI Governance
- Evaluation
- Explainable Artificial Intelligence (XAI)
- Body of Knowledge (BoK) | The Assuring Autonomy International Programme - University of York
- Artificial Intelligence Use Expected to Increase in Risk and Compliance Efforts | Security - BNP Media
Risk
Youtube search... ...Google search
- Learning about risk: Machine learning for risk assessment | N. Paltrinieria, L. Comfort, and G. Reniers - Science Direct
- Machine Learning Security: 3 Risks To Be Aware Of | Rohit Gupta - Plug and Play
- Derisking machine learning and artificial intelligence | B. Babel, K. Buehler, A. Pivonka, B. Richardson, and D. Waldron - McKinsey & Company
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Compliance
Youtube search... ...Google search
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Regulation
Youtube search... ...Google search
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CognitiveScale - Certifai
YouTube Search ...Google search
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