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Machine Learning and AI: An Intuitive Introduction - CFA Institute Masterclass

The document provides an overview of a presentation on machine learning and artificial intelligence, particularly in the finance sector, delivered by Sri Krishnamurthy and moderated by Richard Fernand. It highlights key trends, definitions, use cases, and the transformative impact of AI and ML in financial services, emphasizing the increasing automation and the imperative for organizations to adopt these technologies. Additionally, it covers the workflow involved in machine learning, including data processing, model evaluation, and the importance of interpretability and transparency in AI applications.

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MACHINE LEARNING AND AI: AN INTUITIVE INTRODUCTION 29 April 2020, 9:00 am - 11:00 am EDT Sri Krishnamurthy, CFA President QuantUniversity Richard Fernand, Moderator Senior Director, Global Content, Professional Learning CFA Institute
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Machine Learning and AI: An intuitive Introduction Part 1 2020 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.quantuniversity.com 04/29/2020 CFA Institute - Online
4 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 “The Model-Driven Enterprise” • 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
5 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 Exploration and Experimentation
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
AI and Machine Learning in Finance
8 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
9 Scientists are disrupting the way we live! Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/
10 Interest in Machine learning continues to grow https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
11 MACHINE LEARNING AND AI IS REVOLUTIONIZING FINANCE
12 Market impact at the speed of light! 12
13 • 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 13 1. https://en.wikipedia.org/wiki/Machine_learning 2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
14 Machine Learning & AI in finance: A paradigm shift 14 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
15 The Virtuous Circle of Machine Learning and AI 15 Smart Algorithms Hardware Data
16 The rise of Big Data and Data Science 16 Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
17 Smart Algorithms 17 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
18 Hardware Speed up calculations with 1000s of processors Scale computations with infinite compute power
19 “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
20 http://www.analyticscertificate.com/fintech/
21 http://www.analyticscertificate.com/fintech/
22 http://www.analyticscertificate.com/fintech/
23 http://www.analyticscertificate.com/fintech/
24
Risk Systems That Read® • Northfield uses machine learning based analysis of news text to describe how current conditions in financial markets are different than usual. • Typically, over 8000 articles per day containing more than 20,000 “topics” (companies, industries, countries) are processed. • The nature and magnitudes of these difference are used to revise expectations of financial market risks for all global equities and credit instruments on a daily basis.
26 1. Leveraging large and diverse datasets for Investment decision making at J.P. Morgan1 2. Improving Quantitative investing at AQR2 3. Using Sandboxes and labs to further innovation in fintech at Fidelity3 4. Use of AI and ML increasing in ssset management from idea generation to execution - Wells Fargo4 Additional Use cases 1. https://www.jpmorgan.com/global/cib/research/investment-decisions-using-machine-learning-ai 2. https://www.aqr.com/Learning-Center/Machine-Learning 3. https://www.fidelitylabs.com/ 4. https://www08.wellsfargomedia.com/assets/pdf/personal/investing/investment-institute/IG_Machines_Are_Coming_ADA.pdf
27 Source: https://www.cbinsights.com/research/artificial-intelligence-top-startups/
28 • 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?
#Disrupt19
31 Let’s get under the hood 31 Source: https://www.pikrepo.com/fcsda/yellow-hot-rod-car-with-hood-open
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
33 1. Data 2. Goals 3. Machine learning algorithms 4. Process 5. Performance evaluation Key steps involved
35 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
36 Variables A variable could be: ▫ Categorical – Yes/No flags – AAA,BB ratings for bonds ▫ Numerical – 35 mpg – $170K salary
37 Longitudinal ▫ Observations are dependent ▫ Temporal-continuity is required Cross-sectional ▫ Observations are independent Datasets
38 Data Cross sectional Numerical Categorical Longitudinal Numerical Summary 38
40 • Descriptive Statistics ▫ Goal is to describe the data at hand ▫ Backward-looking ▫ Statistical techniques employed here • Predictive Analytics ▫ Goal is to use historical data to build a model for prediction ▫ Forward-looking ▫ Machine learning & AI techniques employed here Goal 40
41 • Given a dataset, build a model that captures the similarities in different observations and assigns them to different buckets- Clustering • Given a set of variables, predict the value of another variable in a given data set- Prediction ▫ Predict salaries given work experience, education etc. ▫ Predict whether a loan would be approved given fico score, current loans, employment status etc. Predictive Analytics : Cross sectional datasets 41
42 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 Summary 42
44 Machine Learning Unsupervised Supervised Reinforcement Semi-Supervised Machine Learning
45 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 Machine Learning Algorithms 45
46 Supervised Algorithms ▫ Given a set of variables 𝑥!, predict the value of another variable 𝑦 in a given data set such that ▫ If y is numeric => Prediction ▫ If y is categorical => Classification ▫ Example: Given that a customer’s Debt-to-Income ratio increased 20%, what are the chances he/she would default in 3 months? Machine Learning 46 x1,x2,x3… Model F(X) y
47 Unsupervised Algorithms ▫ Given a dataset with variables 𝑥!, build a model that captures the similarities in different observations and assigns them to different buckets => Clustering ▫ Example: Given a list of emerging market stocks, can we segment them into three buckets? Machine Learning 47 Obs1, Obs2,Obs3 etc. Model Obs1- Class 1 Obs2- Class 2 Obs3- Class 1
48 • Parametric models ▫ Assume some functional form ▫ Fit coefficients • Examples : Linear Regression, Neural Networks Supervised Learning models - Prediction 48 𝑌 = 𝛽! + 𝛽" 𝑋" Linear Regression Model Neural network Model
49 • Non-Parametric models ▫ No functional form assumed • Examples : K-nearest neighbors, Decision Trees Supervised Learning models 49 K-nearest neighbor Model Decision tree Model
50 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 Machine Learning Algorithms 50
51 Machine Learning movers and shakers Deep Learning Automatic Machine Learning Ensemble Learning Natural Language Processing Data Robot H20.ai Autosklearn autokkeras Tensorflow Pytorch NLTK HuggingFace Bagging Boosting DNN CNN LSTM GAN
52 http://www.asimovinstitute.org/neural-network-zoo/
54 The Process 54 Data ingestion Data cleansing Feature engineering Training and testing Model building Model selection
55 • What transformations do I need for the x and y variables ? • Which are the best features to use? ▫ Dimension Reduction – PCA ▫ Best subset selection – Forward selection – Backward elimination – Stepwise regression Feature Engineering 55
56 Data Training 80% Testing 20% Training the model 56
58 Evaluating Machine learning algorithms Supervised - Prediction R-square RMS MAE MAPE Supervised- Classification Confusion Matrix ROC Curves Evaluation framework 58
59 • Fit measures in classical regression modeling: • Adjusted 𝑅! has been adjusted for the number of predictors. It increases only when the improve of model is more than one would expect to see by chance (p is the total number of explanatory variables) 𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅! = 1 − ⁄∑"#$ % (𝑦" − 0𝑦")! (𝑛 − 𝑝 − 1) ∑"#$ % 𝑦" − 4𝑦" ! /(𝑛 − 1) • MAE or MAD (mean absolute error/deviation) gives the magnitude of the average absolute error 𝑀𝐴𝐸 = ∑"#$ % 𝑒" 𝑛 Prediction Accuracy Measures
60 ▫ MAPE (mean absolute percentage error) gives a percentage score of how predictions deviate on average 𝑀𝐴𝑃𝐸 = ∑!"# $ 𝑒!/𝑦! 𝑛 ×100% • RMSE (root-mean-squared error) is computed on the training and validation data 𝑅𝑀𝑆𝐸 = 1/𝑛 2 !"# $ 𝑒! % Prediction Accuracy Measures
61 1. Data 2. Goals 3. Machine learning algorithms 4. Process 5. Performance Evaluation Recap
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
63
64
65 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
66 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
67 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”
68 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!
69 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
70 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
#Disrupt19
#Disrupt19 Alternative investments: Interest rate predication for Peer-to-Peer Market places using ML techniques
73 1. Case Intro 2. Data Exploration of the Credit risk data set 3. Problem Definition and Machine learning 4. Performance Evaluation 5. Deployment Case study
74 Credit decisions 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 ?
75 How Lending club works? https://www.lendingclub.com/public/how-peer-lending- works.action
76 • How much should I expect as interest? • Is my borrower credit worthy? • How much interest would a similar borrower pay? • What is the repayment and default rate for a similar borrower? Investor’s big decisions
77 The Data 77 https://www.kaggle.com/wendykan/lending-club-loan-data
78 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
79 79
#Disrupt19 Synthetic VIX data generation using Neural Networks
81 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
82 Missing values • Missing at random • Missing sequences • Need data to fill frames Challenges with real datasets
83 • Access ▫ Hard to find ▫ Rare class problems ▫ Privacy concerns making it difficult to share Challenges with real datasets
84 Imbalanced • Need more samples of rare class • Need proxies for data points that were not observed or recorded Challenges with real datasets
85 Labels • Human labeling is hard • Synthetic label generators Challenges with real datasets
86 VAE https://arxiv.org/pdf/1808.06444.pdf
87 GAN https://developers.google.com/machine- learning/gan/gan_structure
88
89 Demo 1 – Loan Data Synthesizer (Anonymization + Data Augmentation)
90 Demo 2: Synthetic Sales data generation What-if & Scenario analysis
91 Demo 3 : Synthetic VIX generation Extreme scenario generation
1. Key trends in AI, Machine Learning & Fintech 2. An intuitive introduction to AI and ML 3. Case study ▫ Building an interest rate predicator using ML techniques ▫ Synthetic data generation using Neural Networks Part 1: Recap 92
93 To try the QuSandbox and QuSynthesize or any of the other demos, Email us at info@qusandbox.com 93
94 Sign up at: https://www.cfainstitute.org/en/events/w ebinars/machine-learning-and-ai-core- methods-and-applications Signup to Part 2 of this Master class!
Register at https://mlinfinance.splashthat.com Classes start May 12th 95
#Disrupt19
Thank you! Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com Founder and CEO QuantUniversity LLC. srikrishnamurthy www.QuantUniversity.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. 97

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Machine Learning and AI: An Intuitive Introduction - CFA Institute Masterclass

  • 1.
    MACHINE LEARNING ANDAI: AN INTUITIVE INTRODUCTION 29 April 2020, 9:00 am - 11:00 am EDT Sri Krishnamurthy, CFA President QuantUniversity Richard Fernand, Moderator Senior Director, Global Content, Professional Learning CFA Institute
  • 2.
    Use Q&A tosubmit questions for the presenters Use CHAT to share comments and to see what others are saying. You can also select who you would like to send the message to by clicking on the drop down next to To:.
  • 3.
    Machine Learning andAI: An intuitive Introduction Part 1 2020 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.quantuniversity.com 04/29/2020 CFA Institute - Online
  • 4.
    4 Speaker bio • Advisoryand 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 “The Model-Driven Enterprise” • 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
  • 5.
    5 QuantUniversity • Boston-based DataScience, 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 Exploration and Experimentation
  • 6.
    1. Key trendsin 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
  • 7.
    AI and MachineLearning in Finance
  • 8.
    8 The 4th Industrialrevolution 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
  • 9.
    9 Scientists are disruptingthe way we live! Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/
  • 10.
    10 Interest in Machinelearning continues to grow https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
  • 11.
    11 MACHINE LEARNING ANDAI IS REVOLUTIONIZING FINANCE
  • 12.
    12 Market impact atthe speed of light! 12
  • 13.
    13 • Machine learningis 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 13 1. https://en.wikipedia.org/wiki/Machine_learning 2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
  • 14.
    14 Machine Learning &AI in finance: A paradigm shift 14 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
  • 15.
    15 The Virtuous Circleof Machine Learning and AI 15 Smart Algorithms Hardware Data
  • 16.
    16 The rise ofBig Data and Data Science 16 Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
  • 17.
    17 Smart Algorithms 17 Distributing ComputingFrameworks 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
  • 18.
    18 Hardware Speed up calculationswith 1000s of processors Scale computations with infinite compute power
  • 19.
    19 “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
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
    Risk Systems ThatRead® • Northfield uses machine learning based analysis of news text to describe how current conditions in financial markets are different than usual. • Typically, over 8000 articles per day containing more than 20,000 “topics” (companies, industries, countries) are processed. • The nature and magnitudes of these difference are used to revise expectations of financial market risks for all global equities and credit instruments on a daily basis.
  • 26.
    26 1. Leveraging largeand diverse datasets for Investment decision making at J.P. Morgan1 2. Improving Quantitative investing at AQR2 3. Using Sandboxes and labs to further innovation in fintech at Fidelity3 4. Use of AI and ML increasing in ssset management from idea generation to execution - Wells Fargo4 Additional Use cases 1. https://www.jpmorgan.com/global/cib/research/investment-decisions-using-machine-learning-ai 2. https://www.aqr.com/Learning-Center/Machine-Learning 3. https://www.fidelitylabs.com/ 4. https://www08.wellsfargomedia.com/assets/pdf/personal/investing/investment-institute/IG_Machines_Are_Coming_ADA.pdf
  • 27.
  • 28.
    28 • Automation toincrease • 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?
  • 29.
  • 31.
    31 Let’s get underthe hood 31 Source: https://www.pikrepo.com/fcsda/yellow-hot-rod-car-with-hood-open
  • 32.
    Machine Learning Workflow DataScraping/ 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
  • 33.
    33 1. Data 2. Goals 3.Machine learning algorithms 4. Process 5. Performance evaluation Key steps involved
  • 35.
    35 Dataset, variable andObservations 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
  • 36.
    36 Variables A variable couldbe: ▫ Categorical – Yes/No flags – AAA,BB ratings for bonds ▫ Numerical – 35 mpg – $170K salary
  • 37.
    37 Longitudinal ▫ Observations aredependent ▫ Temporal-continuity is required Cross-sectional ▫ Observations are independent Datasets
  • 38.
  • 40.
    40 • Descriptive Statistics ▫Goal is to describe the data at hand ▫ Backward-looking ▫ Statistical techniques employed here • Predictive Analytics ▫ Goal is to use historical data to build a model for prediction ▫ Forward-looking ▫ Machine learning & AI techniques employed here Goal 40
  • 41.
    41 • Given adataset, build a model that captures the similarities in different observations and assigns them to different buckets- Clustering • Given a set of variables, predict the value of another variable in a given data set- Prediction ▫ Predict salaries given work experience, education etc. ▫ Predict whether a loan would be approved given fico score, current loans, employment status etc. Predictive Analytics : Cross sectional datasets 41
  • 42.
    42 Goal Descriptive Statistics Cross sectional Numerical Categorical Numerical vs Categorical Categoricalvs Categorical Numerical vs Numerical Time series Predictive Analytics Cross- sectional Segmentation Prediction Predict a number Predict a category Time-series Summary 42
  • 44.
  • 45.
    45 Goal Descriptive Statistics Cross sectional Numerical Categorical Numerical vs Categorical Categoricalvs Categorical Numerical vs Numerical Time series Predictive Analytics Cross- sectional Segmentation Prediction Predict a number Predict a category Time-series Machine Learning Algorithms 45
  • 46.
    46 Supervised Algorithms ▫ Givena set of variables 𝑥!, predict the value of another variable 𝑦 in a given data set such that ▫ If y is numeric => Prediction ▫ If y is categorical => Classification ▫ Example: Given that a customer’s Debt-to-Income ratio increased 20%, what are the chances he/she would default in 3 months? Machine Learning 46 x1,x2,x3… Model F(X) y
  • 47.
    47 Unsupervised Algorithms ▫ Givena dataset with variables 𝑥!, build a model that captures the similarities in different observations and assigns them to different buckets => Clustering ▫ Example: Given a list of emerging market stocks, can we segment them into three buckets? Machine Learning 47 Obs1, Obs2,Obs3 etc. Model Obs1- Class 1 Obs2- Class 2 Obs3- Class 1
  • 48.
    48 • Parametric models ▫Assume some functional form ▫ Fit coefficients • Examples : Linear Regression, Neural Networks Supervised Learning models - Prediction 48 𝑌 = 𝛽! + 𝛽" 𝑋" Linear Regression Model Neural network Model
  • 49.
    49 • Non-Parametric models ▫No functional form assumed • Examples : K-nearest neighbors, Decision Trees Supervised Learning models 49 K-nearest neighbor Model Decision tree Model
  • 50.
  • 51.
    51 Machine Learning moversand shakers Deep Learning Automatic Machine Learning Ensemble Learning Natural Language Processing Data Robot H20.ai Autosklearn autokkeras Tensorflow Pytorch NLTK HuggingFace Bagging Boosting DNN CNN LSTM GAN
  • 52.
  • 54.
  • 55.
    55 • What transformationsdo I need for the x and y variables ? • Which are the best features to use? ▫ Dimension Reduction – PCA ▫ Best subset selection – Forward selection – Backward elimination – Stepwise regression Feature Engineering 55
  • 56.
  • 58.
    58 Evaluating Machine learning algorithms Supervised - Prediction R-squareRMS MAE MAPE Supervised- Classification Confusion Matrix ROC Curves Evaluation framework 58
  • 59.
    59 • Fit measuresin classical regression modeling: • Adjusted 𝑅! has been adjusted for the number of predictors. It increases only when the improve of model is more than one would expect to see by chance (p is the total number of explanatory variables) 𝐴𝑑𝑗𝑢𝑠𝑡𝑒𝑑 𝑅! = 1 − ⁄∑"#$ % (𝑦" − 0𝑦")! (𝑛 − 𝑝 − 1) ∑"#$ % 𝑦" − 4𝑦" ! /(𝑛 − 1) • MAE or MAD (mean absolute error/deviation) gives the magnitude of the average absolute error 𝑀𝐴𝐸 = ∑"#$ % 𝑒" 𝑛 Prediction Accuracy Measures
  • 60.
    60 ▫ MAPE (meanabsolute percentage error) gives a percentage score of how predictions deviate on average 𝑀𝐴𝑃𝐸 = ∑!"# $ 𝑒!/𝑦! 𝑛 ×100% • RMSE (root-mean-squared error) is computed on the training and validation data 𝑅𝑀𝑆𝐸 = 1/𝑛 2 !"# $ 𝑒! % Prediction Accuracy Measures
  • 61.
    61 1. Data 2. Goals 3.Machine learning algorithms 4. Process 5. Performance Evaluation Recap
  • 62.
    Machine Learning Workflow DataScraping/ 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
  • 63.
  • 64.
  • 65.
    65 Claim: • Machine learningis 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
  • 66.
    66 Claim: • Our modelswork 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
  • 67.
    67 AI and MachineLearning 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”
  • 68.
    68 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!
  • 69.
    69 Claim: • Machine Learningmodels 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
  • 70.
    70 Claim: • Machine Learningand 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
  • 71.
  • 72.
    #Disrupt19 Alternative investments: Interestrate predication for Peer-to-Peer Market places using ML techniques
  • 73.
    73 1. Case Intro 2.Data Exploration of the Credit risk data set 3. Problem Definition and Machine learning 4. Performance Evaluation 5. Deployment Case study
  • 74.
    74 Credit decisions Credit-scoring modelsand 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 ?
  • 75.
    75 How Lending clubworks? https://www.lendingclub.com/public/how-peer-lending- works.action
  • 76.
    76 • How muchshould I expect as interest? • Is my borrower credit worthy? • How much interest would a similar borrower pay? • What is the repayment and default rate for a similar borrower? Investor’s big decisions
  • 77.
  • 78.
    78 Credit Risk pipeline DataIngestion from Lending Club Pre-Processing Feature Engineering Model Development and Tuning Model Deployment Stage 1 Stage 2 Stage 3 Stage 4 Stage 5
  • 79.
  • 80.
    #Disrupt19 Synthetic VIX datageneration using Neural Networks
  • 81.
    81 All scenarios haven’t playedout • Stress scenarios • What-if scenarios Challenges with real datasets Figure ref: http://www.actuaries.org/CTTEES_SOLV/Documents/StressTestingPaper.pdf
  • 82.
    82 Missing values • Missingat random • Missing sequences • Need data to fill frames Challenges with real datasets
  • 83.
    83 • Access ▫ Hardto find ▫ Rare class problems ▫ Privacy concerns making it difficult to share Challenges with real datasets
  • 84.
    84 Imbalanced • Need moresamples of rare class • Need proxies for data points that were not observed or recorded Challenges with real datasets
  • 85.
    85 Labels • Human labelingis hard • Synthetic label generators Challenges with real datasets
  • 86.
  • 87.
  • 88.
  • 89.
    89 Demo 1 –Loan Data Synthesizer (Anonymization + Data Augmentation)
  • 90.
    90 Demo 2: SyntheticSales data generation What-if & Scenario analysis
  • 91.
    91 Demo 3 :Synthetic VIX generation Extreme scenario generation
  • 92.
    1. Key trendsin AI, Machine Learning & Fintech 2. An intuitive introduction to AI and ML 3. Case study ▫ Building an interest rate predicator using ML techniques ▫ Synthetic data generation using Neural Networks Part 1: Recap 92
  • 93.
    93 To try theQuSandbox and QuSynthesize or any of the other demos, Email us at info@qusandbox.com 93
  • 94.
  • 95.
  • 96.
  • 97.
    Thank you! Sri Krishnamurthy,CFA, CAP sri@quantuniversity.com Founder and CEO QuantUniversity LLC. srikrishnamurthy www.QuantUniversity.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. 97