Machine Learning in Finance: 10 Things You Need to Know in 2021
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The document details the kickoff for the QU Winter School 2021 and outlines key trends in AI, machine learning, and fintech. It emphasizes the transformative impact of machine learning in finance and includes information on the speaker's background, Quantuniversity's achievements, and upcoming educational opportunities. Additionally, it touches on the importance of data science, regulatory considerations, and the evolving landscape of financial technologies.
Machine Learning in Finance: 10 Things You Need to Know in 2021
- 1. QU Winter School 2021 Kickoff + 10 things about ML you need to know in 2021 2021 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.quantuniversity.com Jan 27th 2021
- 2. 2 Speaker bio • Advisory and Consultancy for Financial Analytics • Prior Experience at MathWorks, Citigroup and Endeca and 25+ financial services and energy customers. • Columnist for the Wilmott Magazine • Author of forthcoming book “ML in Finance: A pragmatic approach” • Teaches AI/ML and Fintech Related topics in the MS and MBA programs at Northeastern University, Boston • Reviewer: Journal of Asset Management Sri Krishnamurthy Founder and CEO QuantUniversity
- 3. 3 QuantUniversity • Boston-based Data Science, Quant Finance and Machine Learning training and consulting advisory • Trained more than 1000 students in Quantitative methods, Data Science and Big Data Technologies using MATLAB, Python and R • Building a platform for AI and Machine Learning Audit and Experimentation
- 4. 1. Winter school 2021 2. Key trends in AI, Machine Learning & Fintech 3. 10 things you need to know in 2021 4. Demos Agenda
- 5. QuantUniversity Winter School 2021
- 6. 6 • Summer School 2020 Recap 2020
- 7. 7 • Fall School 2020 Recap 2020
- 8. 8 • 13+ countries • 2400+ participants! • Participants from FED, PWC, Goldman Sachs, OCC, E&Y, Deloitte, Genpact, BNP- Paribas, Fidelity, State Street, Discover, Charles Schwab, etc. etc. • Participant profile: ▫ Data Scientists ▫ Quants ▫ Risk Managers ▫ Consultants ▫ Regulators ▫ Students Some numbers!
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- 12. 12 Visit us at www.quantuniversity.com to know more! • Use Code “QuAffiliate” for a $100 off all courses Join us!
- 13. AI and Machine Learning in Finance
- 14. 14 The 4th Industrial revolution is Here! Source: Christoph Roser at AllAboutLean.com As per Wikipedia*, “The 4th Industrial Revolution ….. marked by emerging technology breakthroughs in a number of fields, including robotics, artificial intelligence, nanotechnology, quantum computing, biotechnology, the Internet of Things, the Industrial Internet of Things (IIoT), decentralized consensus, fifth-generation wireless technologies (5G), additive manufacturing/3D printing and fully autonomous vehicles.” * https://en.wikipedia.org/wiki/Fourth_Industrial_Revolution
- 15. 15 Scientists are disrupting the way we live! Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/
- 16. 16 Interest in Machine learning continues to grow https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
- 17. 17 MACHINE LEARNING AND AI IS REVOLUTIONIZING FINANCE
- 18. 18 • Machine learning is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead1 • Artificial intelligence is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals1 Defining Machine Learning and AI 18 1. https://en.wikipedia.org/wiki/Machine_learning 2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
- 19. 19 https://www.federalreserve.gov/supervisionreg/srletters/sr 1107a1.pdf Source: A model consists of three components: an information input component, which delivers assumptions and data to the model; a processing component, which transforms inputs into estimates; a reporting component, which translates the estimates into useful business information
- 21. 21 Machine Learning & AI in finance: A paradigm shift 21 Stochastic Models Factor Models Optimization Risk Factors P/Q Quants Derivative pricing Trading Strategies Simulations Distribution fitting Quant Real-time analytics Predictive analytics Machine Learning RPA NLP Deep Learning Computer Vision Graph Analytics Chatbots Sentiment Analysis Alternative Data Data Scientist
- 22. 22 The Virtuous Circle of Machine Learning and AI 22 Smart Algorithms Hardware Data
- 23. 23 The rise of Big Data and Data Science 23 Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
- 24. 24 Smart Algorithms 24 Distributing Computing Frameworks Deep Learning Frameworks 1. Our labeled datasets were thousands of times too small. 2. Our computers were millions of times too slow. 3. We initialized the weights in a stupid way. 4. We used the wrong type of non-linearity. - Geoff Hinton “Capital One was able to determine fraudulent credit card applications in 100 milliseconds”* * http://go.databricks.com/hubfs/pdfs/Databricks-for-FinTech-170306.pdf
- 25. 25 Hardware Speed up calculations with 1000s of processors Scale computations with infinite compute power
- 26. 26 “Financial Technologies or “Fintech” is used to describe a variety of innovative business models and emerging technologies that have the potential to transform the financial services industry ” Technology drives finance! https://www.iosco.org/library/pubdocs/pdf/IOSCOPD554.pdf
- 31. 31 Source: https://www.cbinsights.com/research/artificial-intelligence-top-startups/
- 34. Machine Learning Workflow Data Scraping/ Ingestion Data Exploration Data Cleansing and Processing Feature Engineering Model Evaluation & Tuning Model Selection Model Deployment/ Inference Supervised Unsupervised Modeling Data Engineer, Dev Ops Engineer Data Scientist/Quants Software/Web Engineer • AutoML • Model Validation • Interpretability Robotic Process Automation (RPA) (Microservices, Pipelines ) • SW: Web/ Rest API • HW: GPU, Cloud • Monitoring • Regression • KNN • Decision Trees • Naive Bayes • Neural Networks • Ensembles • Clustering • PCA • Autoencoder • RMS • MAPS • MAE • Confusion Matrix • Precision/Recall • ROC • Hyper-parameter tuning • Parameter Grids Risk Management/ Compliance(All stages) Analysts & Decision Makers
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- 44. 44 Race to the cloud
- 46. 46 ML as a service
- 50. 50 Heated discussions on AI-Bias, Fairness, Ethics 50
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- 55. 55 Synthetic VIX generation Extreme scenario generation
- 56. 56 To try the QuSandbox and QuSynthesize or any of the other demos, Email us at info@qusandbox.com 56
- 58. Thank you! Sri Krishnamurthy, CFA, CAP Founder and CEO QuantUniversity LLC. srikrishnamurthy www.QuantUniversity.com Contact Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be distributed or used in any other publication without the prior written consent of QuantUniversity LLC. 58