Adopting Data Science and Machine Learning in the financial enterprise
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Financial firms are taking AI and machine learning seriously to augment traditional investment decision making. Alternative datasets including text analytics, cloud computing, algorithmic trading are game changers for many firms who are adopting technology at a rapid pace. As more and more open-source technologies penetrate enterprises, quants and data scientists have a plethora of choices for building, testing and scaling quantitative models. Even though there are multiple solutions and platforms available to build machine learning solutions, challenges remain in adopting machine learning in the enterprise.In this talk we will illustrate a step-by-step process to enable replicable AI/ML research within the enterprise using QuSandbox.
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Adopting Data Science and Machine Learning in the financial enterprise
- 1. Adopting Data Science and Machine Learning in the Enterprise 2018 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.analyticscertificate.com
- 2. 2 About us: • Data Science, Quant Finance and Machine Learning Startup • Technologies using MATLAB, Python and R • Programs ▫ Analytics Certificate Program ▫ Fintech programs • Platform
- 3. • Founder of QuantUniversity LLC. and www.analyticscertificate.com • Advisory and Consultancy for Financial Analytics • Prior Experience at MathWorks, Citigroup and Endeca and 25+ financial services and energy customers. • Regular Columnist for the Wilmott Magazine • Author of forthcoming book “Financial Modeling: A case study approach” published by Wiley • Charted Financial Analyst and Certified Analytics Professional • Teaches Analytics in the Babson College MBA program and at Northeastern University, Boston Sri Krishnamurthy Founder and CEO 3
- 5. 5 AI and ML in Finance
- 7. 7 How did we get here?
- 8. 8
- 9. 9 • “AI is the theory and development of computer systems able to perform tasks that traditionally have required human intelligence. • AI is a broad field, of which ‘machine learning’ is a sub-category” What is Machine Learning and AI? Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
- 10. 10 Machine Learning & AI in finance – A paradigm shift 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
- 11. 11 The Virtuous Circle of Machine Learning and AI Smart Algorithms Hardware Data
- 12. 12 The rise of Big Data and Data Science Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
- 13. 13 Smarter Algorithms Parallel and 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
- 14. 14 Hardware
- 15. 15 A framework for evaluating your organization’s appetite for AI and machine learning Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
- 16. 16
- 18. 18 Goal Descriptive Statistics Cross sectional Numerical Categorical Numerical vs Categorical Categorical vs Categorical Numerical vs Numerical Time series Predictive Analytics Cross- sectional Segmentation Prediction Predict a number Predict a category Time-series Goal
- 19. 19 Machine Learning Algorithms Machine Learning Supervised Prediction Parametric Linear Regression Neural Networks Non- parametric KNN Decision Trees Classification Parametric Logistic Regression Neural Networks Non Parametric Decision Trees KNN Unsupervised algorithms K-means Associative rule mining
- 21. 21 Evaluating Machine learning algorithms Supervised - Prediction R-square RMS MAE MAPE Supervised- Classification Confusion Matrix ROC Curves Evaluation framework
- 22. 22
- 23. 23
- 24. 24 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
- 25. 25 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
- 26. 26 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”
- 27. 27 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!
- 28. 28 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
- 29. 29 Claim: • Machine Learning and AI will replace humans in most applications Caution: • Beware of the hype! • Just because it worked some times 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. Are we there yet? https://www.bloomberg.com/news/articles/2017-10-20/automation- starts-to-sweep-wall-street-with-tons-of-glitches
- 30. 30
- 31. 31 • Understanding sentiments in Earnings call transcripts Goal
- 32. 32 • Interpreting emotions • Labeling data Challenges
- 33. 33 What is NLP ? AI Linguistics Computer Science
- 34. 34 • Q/A • Dialog systems - Chatbots • Topic summarization • Sentiment analysis • Classification • Keyword extraction - Search • Information extraction – Prices, Dates, People etc. • Tone Analysis • Machine Translation • Document comparison – Similar/Dissimilar Sample applications
- 36. 36 • If computers can understand language, opens huge possibilities ▫ Read and summarize ▫ Translate ▫ Describe what’s happening ▫ Understand commands ▫ Answer questions ▫ Respond in plain language Language allows understanding
- 37. 37 • Describe rules of grammar • Describe meanings of words and their relationships • …including all the special cases • ...and idioms • ...and special cases for the idioms • ... • ...understand language! Traditional language AI https://en.wikipedia.org/wiki/Formal_language
- 38. 38 What is NLP ? Jumping NLP Curves https://ieeexplore.ieee.org/document/6786458/
- 39. 39 Q: What’s hard about writing programs to understand text?
- 40. 40 • Ambiguity: ▫ “ground” ▫ “jaguar” ▫ “The car hit the pole while it was moving” ▫ “One morning I shot an elephant in my pajamas. How he got into my pajamas, I’ll never know.” ▫ “The tank is full of soldiers.” “The tank is full of nitrogen.” Language is hard to deal with
- 41. 41
- 42. 42 • Many ways to say the same thing ▫ “the same thing can be said in many ways” ▫ “language is versatile” ▫ “The same words can be arranged in many different ways to express the same idea” ▫ … Language is hard to deal with
- 43. 43 • APIs • Human Insight • Expert Knowledge • Build your own Options?
- 44. 44 NLP pipeline Data Ingestion from Edgar Pre-Processing Invoking APIs to label data Compare APIs Build a new model for sentiment Analysis
- 45. 45 Building a model vs Deploying a model
- 46. QuSandbox- The platform for adopting Data Science and AI in the Enterprise 2018 Copyright QuantUniversity LLC.
- 47. 47 • QuSandbox, is an end-to-end workflow based system to enable creation and deployment of data science workflows within the enterprise for primarily ML and AI applications. • Our environment supports AWS and Google Cloud platform and incorporates model and data provenance throughout the life cycle of model development. • The solution can also be hosted on-prem to leverage custom hardware and software integrations. Executive Summary
- 49. 49 What’s needed for reproducibility Code Data Environment Process
- 50. 50 Prototype Standardize workflow Productionize and share Model Management with QuSandbox
- 51. 51 QuSandbox solution suite for ML/AI applications Model Analytics Studio QuSandbox Research hub
- 53. 54 • The regulatory sandbox allows businesses to test innovative products, services, business models and delivery mechanisms in the real market, with real consumers. • The sandbox is a supervised space, open to both authorized and unauthorized firms, that provides firms with: ▫ reduced time-to-market at potentially lower cost ▫ appropriate consumer protection safeguards built in to new products and services ▫ better access to finance • https://www.fca.org.uk/firms/regulatory-sandbox Regulatory Sandboxes
- 54. 55 Quant/Enterprise use cases • Create an environment that can support multiple platforms and programming languages • Enable remote running of applications • Ability to try out a Github submission/ someone else’s code • Facilitate creation of Docker images to create replicable containers • Create prototyping environments for Data Science/Quant teams • Enable Data scientists/Quants to deploy their solutions • Enable running multiple tasks and jobs • Enable concurrent running of multiple experiments • Integrate seamlessly with the cloud to scale up computations Use cases
- 55. 56 Fintech use cases • To demonstrate solutions to enterprises • Create customized enterprise trials for companies that don’t permit installation of vendor software prior to procurement • To manage quick updates • Enable effective integration and hosting of services (REST APIs) • To deploy custom services on QuSandbox Use cases
- 56. 57 Academic use cases • Enable creation of course material and exercises that could be shared • Enable students and workshop participants to focus on the data science experiments rather than environment setting Use cases
- 57. 58 ResearchHub
- 58. 59 Research hub - Process
- 62. 63 JDF- DSL
- 63. 64 QuSandbox
- 65. 66 Creating replicable environments Creating and manage replicable environments (Code + software + data) in a single portal
- 66. 67 Creating replicable environments Create replicable environments (Code + software + data) through a easy point & click tool and publish to Dockerhub or manage internally Share it with target users
- 67. 68 User portal • Run multiple experiments in pre-created environments (Code + software + data) • Deploy your own solutions • Run any Docker image or Github submission on the cloud
- 68. 69 Run Jupyter notebooks and prototype applications
- 69. 70 Run Rstudio and Shiny applications
- 70. 71 Run any Docker application
- 71. 72 Manage tasks and errors
- 72. 73 User portal • Dockerize and deploy applications on AWS in just a few steps
- 73. 74 Deploy applications with ease
- 77. Sri Krishnamurthy, CFA, CAP Founder and Chief Data Scientist QuantUniversity LLC. srikrishnamurthy www.QuantUniversity.com www.analyticscertificate.com www.qusandbox.com 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. 78