Ml master class
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The document presents an overview of machine learning and AI in finance, highlighting trends, methodologies, and case studies related to peer-to-peer lending and synthetic data generation. It underscores the potential of these technologies to transform the financial services industry while emphasizing the importance of interpretability and risk management in deploying models. The presentation by Sri Krishnamurthy from Quant University outlines the current landscape and challenges faced in integrating AI and machine learning into financial applications.
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AI and Machine Learning in Production
https://www.itnews.com.au/news/hsbc-societe-generale-run-
into-ais-production-problems-477966
Kristy Roth from HSBC:
“It’s been somewhat easy - in a funny way - to
get going using sample data, [but] then you hit
the real problems,” Roth said.
“I think our early track record on PoCs or pilots
hides a little bit the underlying issues.
Matt Davey from Societe Generale:
“We’ve done quite a bit of work with RPA
recently and I have to say we’ve been a bit
disillusioned with that experience,”
“the PoC is the easy bit: it’s how you get that
into production and shift the balance”](https://image.slidesharecdn.com/mlmasterclass-cfauae2020-201010132737/85/Ml-master-class-31-320.jpg)
Ml master class
- 1. Machine Learning and AI An intuitive Introduction 2020 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.qu.academy Oct 10th, 2020 Online
- 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 “Pragmatic AI and ML in Finance” • 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 Experimentation
- 4. 1. Key trends in AI, Machine Learning & Fintech 2. An intuitive introduction to AI and ML 3. Case study ▫ Alternative investments: Interest rate predication for Peer-to-Peer Market places using ML techniques ▫ Scenario analysis: Synthetic VIX data generation using Neural Networks Agenda
- 5. AI and Machine Learning in Finance
- 6. 6 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
- 7. 7 Scientists are disrupting the way we live! Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/
- 8. 8 Interest in Machine learning continues to grow https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
- 9. 9 MACHINE LEARNING AND AI IS REVOLUTIONIZING FINANCE
- 10. 10 Market impact at the speed of light! 10
- 11. 11 • 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 11 1. https://en.wikipedia.org/wiki/Machine_learning 2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
- 12. 12 Machine Learning & AI in finance: A paradigm shift 12 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
- 13. 13 The Virtuous Circle of Machine Learning and AI 13 Smart Algorithms Hardware Data
- 14. 14 The rise of Big Data and Data Science 14 Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
- 15. 15 Smart Algorithms 15 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
- 16. 16 Hardware Speed up calculations with 1000s of processors Scale computations with infinite compute power
- 17. 17 “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
- 22. 22 Source: https://www.cbinsights.com/research/artificial-intelligence-top-startups/
- 23. 23 • Automation to increase • Digital transformation and move to the cloud finally happening • Use of Synthetic data to increase • Edge cases of AI put to truth test! • Fintechs feeling the pressure to prove themselves! • Human-in-the-loop AI to regain focus! The changes have been drastic and sudden! What’s in store for the industry is yet to be seen! What does Covid2019 mean to adoption of AI and ML in Financial services?
- 25. 25 Let’s get under the hood 25 Source: https://www.pikrepo.com/fcsda/yellow-hot-rod-car-with-hood-open
- 26. 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/QuantsSoftware/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& DecisionMakers
- 27. 27
- 28. 28
- 29. 29 Claim: • Machine learning is better for fraud detection, looking for arbitrage opportunities and trade execution Caution: • Beware of imbalanced class problems • A model that gives 99% accuracy may still not be good enough 1. Machine learning is not a generic solution to all problems
- 30. 30 Claim: • Our models work on datasets we have tested on Caution: • Do we have enough data? • How do we handle bias in datasets? • Beware of overfitting • Historical Analysis is not Prediction 2. A prototype model is not your production model
- 31. 31 AI and Machine Learning in Production https://www.itnews.com.au/news/hsbc-societe-generale-run- into-ais-production-problems-477966 Kristy Roth from HSBC: “It’s been somewhat easy - in a funny way - to get going using sample data, [but] then you hit the real problems,” Roth said. “I think our early track record on PoCs or pilots hides a little bit the underlying issues. Matt Davey from Societe Generale: “We’ve done quite a bit of work with RPA recently and I have to say we’ve been a bit disillusioned with that experience,” “the PoC is the easy bit: it’s how you get that into production and shift the balance”
- 32. 32 Claim: • It works. We don’t know how! Caution: • It’s still not a proven science • Interpretability or “auditability” of models is important • Transparency in codebase is paramount with the proliferation of opensource tools • Skilled data scientists who are knowledgeable about algorithms and their appropriate usage are key to successful adoption 3. We are just getting started!
- 33. 33 Claim: • Machine Learning models are more accurate than traditional models Caution: • Is accuracy the right metric? • How do we evaluate the model? RMS or R2 • How does the model behave in different regimes? 4. Choose the right metrics for evaluation
- 34. 34 Claim: • Machine Learning and AI will replace humans in most applications Caution: • Beware of the hype! • Just because it worked sometimes doesn’t mean that the organization can be on autopilot • Will we have true AI or Augmented Intelligence? • Model risk and robust risk management is paramount to the success of the organization. • We are just getting started! 5. The Robots are coming! https://www.bloomberg.com/news/articles/2017-10-20/automation- starts-to-sweep-wall-street-with-tons-of-glitches
- 35. #Disrupt19 Alternative investments: Interest rate predication for Peer-to-Peer Market places using ML techniques
- 36. 36 How Lending club works? https://www.lendingclub.com/public/how-peer-lending- works.action
- 38. 38 Credit Risk pipeline Data Ingestion from Lending Club Pre-Processing Feature Engineering Model Development and Tuning Model Deployment Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
- 39. 39 39
- 40. #Disrupt19 Synthetic VIX data generation using Neural Networks
- 41. 41 All scenarios haven’t played out • Stress scenarios • What-if scenarios Challenges with real datasets Figure ref: http://www.actuaries.org/CTTEES_SOLV/Documents/StressTestingPaper.pdf
- 42. 42 Missing values • Missing at random • Missing sequences • Need data to fill frames Challenges with real datasets
- 43. 43 • Access ▫ Hard to find ▫ Rare class problems ▫ Privacy concerns making it difficult to share Challenges with real datasets
- 44. 44 Imbalanced • Need more samples of rare class • Need proxies for data points that were not observed or recorded Challenges with real datasets
- 45. 45 Labels • Human labeling is hard • Synthetic label generators Challenges with real datasets
- 47. 47
- 48. 48 Demo: Synthetic VIX generation Extreme scenario generation
- 50. Thank you! Sri Krishnamurthy, CFA, CAP Founder and CEO QuantUniversity LLC. srikrishnamurthy www.qu.academy 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. 50