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Ai in finance

Ai in finance

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Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.


In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
AI in Finance

Innovations in technology has revolutionized financial services to an extent that large financial institutions like Goldman Sachs are claiming to be technology companies! It is no secret that technological innovations like Data science and AI are changing fundamentally how financial products are created, tested and delivered. While it is exciting to learn about technologies themselves, there is very little guidance available to companies and financial professionals should retool and gear themselves towards the upcoming revolution.


In this master class, we will discuss key innovations in Data Science and AI and connect applications of these novel fields in forecasting and optimization. Through case studies and examples, we will demonstrate why now is the time you should invest to learn about the topics that will reshape the financial services industry of the future!
AI in Finance

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Ai in finance

  1. 1. AI and Machine Learning in Finance 2019 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.analyticscertificate.com
  2. 2. 2
  3. 3. 3 • “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
  4. 4. 4 What is Machine Learning and AI? Source: Mckinsey Report: An executive guide to AI
  5. 5. 5 What is Machine Learning and AI?
  6. 6. 6 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
  7. 7. 7 A framework for evaluating your organization’s appetite for AI and machine learning Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
  8. 8. 8 Sizing the opportunity Source: Mckinsey Report: Notes from the AI frontier: Insights from hundreds of use cases
  9. 9. 9
  10. 10. 10 Data Cross sectional Numerical Categorical Longitudinal Numerical Handling Data
  11. 11. 11 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
  12. 12. 12 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
  13. 13. 13 The Process Data cleansing Feature Engineering Training and Testing Model building Model selection
  14. 14. 14 Evaluating Machine learning algorithms Supervised - Prediction R-square RMS MAE MAPE Supervised- Classification Confusion Matrix ROC Curves Evaluation framework
  15. 15. 15
  16. 16. 16
  17. 17. 17 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
  18. 18. 18 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
  19. 19. 19 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”
  20. 20. 20 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!
  21. 21. 21 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
  22. 22. 22 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
  23. 23. 23
  24. 24. Credit risk in consumer credit Credit-scoring models and techniques assess the risk in lending to customers. Typical decisions: • Grant credit/not to new applicants • Increasing/Decreasing spending limits • Increasing/Decreasing lending rates • What new products can be given to existing applicants ?
  25. 25. Credit assessment in consumer credit History: • Gut feel • Social network • Communities and influence Traditional: • Scoring mechanisms through credit bureaus • Bank assessments through business rules Newer approaches: • Peer-to-Peer lending • Prosper Market place
  26. 26. 26 The Data https://www.kaggle.com/wendykan/lending-club-loan-data
  27. 27. Dataset, variable and Observations Dataset: A rectangular array with Rows as observations and columns as variables Variable: A characteristic of members of a population ( Age, State etc.) Observation: List of Variable values for a member of the population
  28. 28. Variable description
  29. 29. 29 • Supervised Algorithms ▫ Given a set of variables !", predict the value of another variable # in a given data set such that ▫ If y is numeric => Prediction ▫ If y is categorical => Classification Machine Learning x1,x2,x3… Model F(X) y
  30. 30. 30 • Parametric models ▫ Assume some functional form ▫ Fit coefficients • Examples : Linear Regression, Neural Networks Supervised Learning models - Prediction ! = #$ + #&'& Linear Regression Model Neural network Model
  31. 31. 31 • Non-Parametric models ▫ No functional form assumed • Examples : K-nearest neighbors, Decision Trees Supervised Learning models K-nearest neighbor Model Decision tree Model
  32. 32. 32 The Workflow Data cleansing Feature Engineering Training and Testing Model building Model selection Model Deployment
  33. 33. 33 • Automated machine learning (AutoML) is the process of automating the end-to-end process of applying machine learning to real-world problems. AutoML
  34. 34. 34 • Automated Feature Engineering ▫ Feature selection ▫ Feature extraction ▫ Meta learning and transfer learning ▫ Detection and handling of skewed data and/or missing values • Hyper-parameter optimization • Model Selection • Reference: https://en.wikipedia.org/wiki/Automated_machine_learning Types of frameworks
  35. 35. 35 • Parameters: Values that can be estimated from data ▫ Examples: – Regression Coefficients – Weights in a Neural Network • HyperParameters: Values external to the model and cannot be learnt from the data ▫ Examples: – Learning rate in Neural Network – Regularization parameters Parameters vs Hyper Parameters
  36. 36. 36 • Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal model which minimizes a predefined loss function on given independent data.[1] • [1] Claesen, Marc; Bart De Moor (2015). "Hyperparameter Search in Machine Learning". • Image from: https://support.sas.com/resources/papers/proceedings17/SAS0514-2017.pdf Hyperparameter optimization
  37. 37. 37 • Interpretability: Ability of users to understand the model, the parameters of the model and their effect on the outcome • Example: ▫ In regression, coefficients enable us to interpret the influence of an independent variable on the dependent variable. ▫ The standard error of estimates of the coefficients enable us to determine how confident are we on these estimates Model selection considerations
  38. 38. 38 • Parsimonious models: A parsimonious model is a model that accomplishes a desired level of explanation or prediction with as few predictor variables as possible. • Example: ▫ In regression, using Exhaustive search, Forward search, Backward search or Stepwise regression in model selection ▫ Using PCA on the feature space prior to model building Model selection considerations
  39. 39. 39 • Ensemble models: Ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone. Image from: https://blogs.sas.com/content/subconsciousmusings/2017/05/18/sta cked-ensemble-models-win-data-science-competitions/ Model selection considerations
  40. 40. 40 Full pipeline Auotmation • AutoWEKA is an approach for the simultaneous selection of a machine learning algorithm and its hyperparameters; combined with the WEKA package it automatically yields good models for a wide variety of data sets. • Auto-sklearn is an extension of AutoWEKA using the Python library scikit- learn which is a drop-in replacement for regular scikit-learn classifiers and regressors. It improves over AutoWEKA by using meta-learning to increase search efficiency and post-hoc ensemble building to combine the models generated during the hyperparameter optimization process. • TPOT is a data-science assistant which optimizes machine learning pipelines using genetic programming. Ref: https://www.ml4aad.org/automl/ Frameworks
  41. 41. 41 Hyper-parameter optimization and Model Selection • H2O AutoML provides automated model selection and ensembling for the H2O machine learning and data analytics platform. • mlr is a R package that contains several hyperparameter optimization techniques for machine learning problems. Ref: https://www.ml4aad.org/automl/ Frameworks
  42. 42. 42 Deep Neural Network Architecture search • Google CLOUD AUTOML is an could-based machine learning service which so far provides the automated generation of computer vision pipelines. • Auto Keras is an open-source python package for neural architecture search. • Ref: ▫ https://www.ml4aad.org/automl/ ▫ https://en.wikipedia.org/wiki/Automated_machine_learning Frameworks
  43. 43. 43 Hardware Considerations
  44. 44. 44 Hardware Considerations Reference: https://azure.microsoft.com/en-us/blog/release- models-at-pace-using-microsoft-s-automl/
  45. 45. 45 www.QuSandbox.com
  46. 46. 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. 46

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