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Fintech day 2 final

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Fintech day 2

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Fintech day 2 final

  1. 1. Location: Boston Fintech Week 2019 Babson College Boston, MA Fintech Bootcamp Day 2 2019 Copyright QuantUniversity LLC. Presented By: Sri Krishnamurthy, CFA, CAP sri@quantuniversity.com www.analyticscertificate.com
  2. 2. 2 QuantUniversity • Analytics and Fintech Advisory • Trained more than 1000 students in Quantitative methods, Data Science and Big Data & Fintech • Programs ▫ Analytics Certificate Program ▫ Fintech Certification program • Building
  3. 3. • Founder of QuantUniversity LLC. and www.analyticscertificate.com • Advisory and Consultancy specializing in Data Science, ML and Analytics • Prior Experience at MathWorks, Citigroup and Endeca and 25+ financial services and energy customers. • Charted Financial Analyst and Certified Analytics Professional • Teaches Analytics in the Babson College and at Northeastern University, Boston Sri Krishnamurthy Founder and CEO 3
  4. 4. 4 Agenda – Day 1
  5. 5. 5 Agenda – Day 2
  6. 6. Insurtech
  7. 7. 7 Process Optimization & Automation Channel partnerships New products due to demands Geographic expansion & customization to local needs Connected devices: Increased use of analytics, behavior tracking & use of Telematics Mobile, Online digital channels On-demand & new products for novel use cases Beyond typical PC/Life Insurance products Corporate Consumer Key innovations
  8. 8. 8 Case 1 – Roof type classification
  9. 9. 9 Case 1 – Roof type classification
  10. 10. 10 Insurtech Insights
  11. 11. 40 WALL STREET, NEW YORK, NY www.rblt.com KEY INSURTECH INSIGHTS Sep 2019 Dushyant Shahrawat Director, FinTech Investment Banking
  12. 12. www.rblt.comwww.rblt.com What Is It? 12 PERSONAL LINES COMMERCIAL LINES Auto Home Property & Casualty Workers Compensation LifeHealth Group Health and Life Cyber AND THEN THERE IS… Wedding cold feet, Alien abduction, Body part, Chicken insurance
  13. 13. www.rblt.comwww.rblt.com How Large Is It? 13Source – InsureTech Connect, Oliver Wyman Insurance Sector Size By Premiums Written
  14. 14. www.rblt.comwww.rblt.com How Is The Process Changing? 14 Quote Issue Bind Pricing Underwriting Claims & Settlement Policy Admin & Central Systems • New Sales Models • Shopping Sites • Telematics, IoT • Alternative Data • Chatbots • New Processing Systems • Drones • Digital Disbursements
  15. 15. www.rblt.comwww.rblt.com What Are The New InsurTechs Doing? 15
  16. 16. www.rblt.comwww.rblt.com Key Insights 16 InsurTech is in a multi-year growth phase attracting record amounts of capital since 2014 § Unlike other FinTech verticals that show signs of slowdown, Series A deal activity is still robust in InsurTech § At a projected $5B for full year 2019, financing could set a record this year § Funding has grown at 60% CAGR over 2014- 1H’2018 Strategic investors are playing a much larger role in InsurTech financing than in other FinTech verticals § Strategic investors participated in over 50% of InsurTech financings in Q2 2019 (versus 35% in other verticals) § SoftBank was the biggest investor in Q2 2019: Lemonade ($300M), Collective Health ($205M), Policy Bazaar ($152M) Good news for InsurTech CEOs. The size of funding rounds and valuations are both rising § Median equity capital raised is growing over funding rounds: median Series A is $11M while Series C is $51M § Median valuations for Series A have gone up the most: 2.5x from $17M (2014) to $43M (2019). Series B valuations have also risen in line with Series A while Series C is also increased, although at a more measured pace than Series A/B. Like in other FinTech verticals, B2C InsurTechs (except Life insurance) attract more funding than B2B firms § Two consumer-facing sectors, Health (53%) and Personal Lines (26%) have attracted 80% of funding § Life, Commercial and Multiline are still in early stages, attracting between 5-8% of total capital § While B2C InsurTechs (Health, Personal Lines) attract more funding, B2B InsurTechs (P&C) attract greater interest from acquirers M&A acquirers are growing in number and variety including PEs, Strategics, Tech firms and even Telcos § InsurTech CEOs have a growing list of potential acquirers to sell their firms to (310 and growing) § Carriers have become active buyers of InsurTechs, especially Allianz, AXA and MunichRe Median pre-money valuations vary widely by subsector indicating big differences in investor appetite § Personal Lines (Auto, Home) are valued at $55M, followed by Health, which is valued at $37M § Commercial, Multiline and Life valuations fall within a narrow range ($26M, $24M, $21M respectively) 1 5 6 2 4 3
  17. 17. www.rblt.comwww.rblt.com $737 $2,790 $2,460 $4,340 $4,730 $2,510 $0 $1,000 $2,000 $3,000 $4,000 $5,000 $6,000 2014 2015 2016 2017 2018 2019e InsurTech Continues To Attract Significant Funding 17 C a p i ta l R a i s e ($ Millions) CAGR: 60% Largest Capital Raise Company Select Investor 88 119 $80M Series C $720M Pre- Money 154 200 $934M PE Growth ~$7B Pre-Money Undisclosed 196 274 $500M Series A ~$2.5B Pre-Money 252 389 $160M Series B $240M Pre- Money 265 393 $950M PE Growth ~$2.2B Pre-Money 131 165 $500M Series E > $1B Pre-Money Deals (w/ Reported $ Value) Total Deals 2H’2019 (Annualized)1H’2019 (Actual) Source – RBLT analysis, Pitchbook, FactSet, CrunchBase, CB Insights, other industry sources # of Companies (CY’14 – Q2’2019) 738 # of Deals (w/ Reported $ Value) (CY’14 – Q2’2019) 1,086 Total Deal Value (w/ Reported $ Value) (CY’14 – Q2’2019) $17.6B Unicorns Created (w/ Reported Valuation) (CY’14 – Q2’2019) 16
  18. 18. www.rblt.comwww.rblt.com Funding League Table Highlights Sector Vibrancy 18 $3,818 $1,755 $1,279 $1,034 $950$925 $598 $521$480$464$442$369$355$285$220$216$210$178$164$154 To p 2 0 I n s u r Te c h s by To ta l C a p i ta l R a i s e ($ Millions | As of Q2’2019) LH&A LH&A P&C LH&A LH&A LH&A ML P&C MLLH&A P&C P&C ML LH&A P&C LH&A LH&A ML LH&A LH&A Key: LH&A (Life, Health, and Annuity) | P&C (Retail and Commercial Lines) | ML (Multiline Insurers, Line of Business Agnostic)
  19. 19. www.rblt.comwww.rblt.com $55M, $10M, $1,229M $26M, $5M, $411M $24M, $10M, $232M $37M, $10M, $2,538M $21M, $9M, $335M $4M $5M $6M $7M $8M $9M $10M $11M $12M $10M $15M $20M $25M $30M $35M $40M $45M $50M $55M $60M MedianCapitalRaise Median Valuation (Pre-Money) B2C Models Attract More Investor Interest Than B2B 19 35 44 P&C (Personal Lines) 35 41 P&C (Commercial Lines) 9 13 Life & Annuity 34 41 Health Company Count Deal Count 20 25 Multiline R e l a t i v e Va l u a t i o n a n d C a p i t a l I n f l o w B y S u b s e c t o r (United States | CY’18 – Q2’19 | VC & Growth Equity) KEY • Median Valuation, Median Capital Raise, Total Capital Invested • Relative size of bubble represents “Total Capital Invested” Note: Life & Annuity (Health IQ’s Series D, E valuation figures excluded from analysis) Source – RBLT analysis, Pitchbook, FactSet, CrunchBase, CB Insights, other industry sources Life & Annuity Multiline Health P&C (Personal Lines) P&C (Commercial Lines)
  20. 20. www.rblt.comwww.rblt.com 45 46 50 107 119 32 0 20 40 60 80 100 120 140 CY'2014 CY'2015 CY'2016 CY'2017 CY'2018 CY'2019e M&A Is Led By Strategic Investors 20 M & A A c t i v i t y (PE, Strategic M&A) Select Large Deal Target Acquirer 51% $1,800B ↑ 2 % 72% $760M ↑ 9 % 56% $7,500M ↑ 114 % 64% $3,000M ↑ 11 % 71% $1,578M ↓ 46 % 56% $1,400M CAGR: 28% % ∆ YoY % Strategic M&A # of Deals (CY’14 – Q2’2019) 400 Total Deal Value (w/ Reported $ Value) (CY’14 – Q2’2019) $51.7B 2H’2019 (Annualized)1H’2019 (Actual) Source – RBLT analysis, Pitchbook, FactSet, CrunchBase, CB Insights, other industry sources
  21. 21. www.rblt.comwww.rblt.com 21 Please Contact Us To Discuss More: Vikas Shah I n v e s t m e n t B a n k i n g 2 1 2 - 6 0 7 - 3 1 0 0 v s h a h @ r b l t . c o m Dushyant “D” Shahrawat I n v e s t m e n t B a n k i n g 2 1 2 - 6 0 7 - 3 1 8 0 d s h a h r a w a t @ r b l t . c o m Copyright 2019. Rosenblatt Securities Inc. All rights reserved. Rosenblatt Securities Inc. seeks to provide and receive remuneration for Agency Brokerage, Market Structure Analysis, Macro and other Sector Analysis and Investment Banking Advisory Services. Rosenblatt Securities Inc. may, from time to time, provide these services to companies mentioned in this analysis. This material is not a research report and should not be construed as such, and does not contain enough information to support an investment decision. Neither the information contained herein, nor any opinion expressed herein, constitutes the recommendation or solicitation of the purchase or sale of any securities or commodities. The information herein was obtained from sources which Rosenblatt Securities Inc. believes reliable, but we do not guarantee its accuracy. No part of this material may be duplicated in any form by any means. Member NYSE, FINRA, SIPC.
  22. 22. AI and Machine Learning in Finance
  23. 23. 23 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
  24. 24. 24 Scientists are disrupting the way we live! Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/
  25. 25. 25 Interest in Machine learning continues to grow https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf
  26. 26. 26 MACHINE LEARNING AND AI IS REVOLUTIONIZING FINANCE
  27. 27. 27 Market impact at the speed of light! 27
  28. 28. 28 Machine Learning & AI in finance: A paradigm shift 28 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
  29. 29. 29 CFA Institute has adopted Fintech and AI content in its curriculum Ref: https://www.cfainstitute.org/-/media/documents/support/programs/cfa/cfa-program-level-iii-fintech-in-investment-management.ashx
  30. 30. 30 The Virtuous Circle of Machine Learning and AI 30 Smart Algorithms Hardware Data
  31. 31. 31 The rise of Big Data and Data Science 31 Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg
  32. 32. 32 Smart Algorithms 32 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
  33. 33. 33 Hardware Speed up calculations with 1000s of processors Scale computations with infinite compute power
  34. 34. 35 • 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 Definitions: Machine Learning and AI 35 1. https://en.wikipedia.org/wiki/Machine_learning 2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
  35. 35. 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
  36. 36. 37 1. Data 2. Goals 3. Machine learning algorithms 4. Process 5. Performance evaluation Key steps involved
  37. 37. 39 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
  38. 38. 40 Variables A variable could be: ▫ Categorical – Yes/No flags – AAA,BB ratings for bonds ▫ Numerical – 35 mpg – $170K salary
  39. 39. 41 Longitudinal ▫ Observations are dependent ▫ Temporal-continuity is required Cross-sectional ▫ Observations are independent Datasets
  40. 40. 42 Data Cross sectional Numerical Categorical Longitudinal Numerical Summary 42
  41. 41. 44 • 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 44
  42. 42. 45 • 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 45
  43. 43. 46 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 46
  44. 44. 48 Machine Learning Unsupervised Supervised Reinforcement Semi-Supervised Machine Learning
  45. 45. 49 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 49
  46. 46. 50 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 50 x1,x2,x3… Model F(X) y
  47. 47. 51 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 51 Obs1, Obs2,Obs3 etc. Model Obs1- Class 1 Obs2- Class 2 Obs3- Class 1
  48. 48. 52 • Parametric models ▫ Assume some functional form ▫ Fit coefficients • Examples : Linear Regression, Neural Networks Supervised Learning models - Prediction 52 𝑌 = 𝛽' + 𝛽) 𝑋) Linear Regression Model Neural network Model
  49. 49. 53 • Non-Parametric models ▫ No functional form assumed • Examples : K-nearest neighbors, Decision Trees Supervised Learning models 53 K-nearest neighbor Model Decision tree Model
  50. 50. 54 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 54
  51. 51. 55 Machine Learning movers and shakers Deep Learning Automatic Machine Learning Ensemble Learning Natural Language Processing
  52. 52. 56 http://www.asimovinstitute.org/neural-network-zoo/
  53. 53. 58 The Process 58 Data ingestion Data cleansing Feature engineering Training and testing Model building Model selection
  54. 54. 59 • 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 59
  55. 55. 60 Data Training 80% Testing 20% Training the model 60
  56. 56. 62 Evaluating Machine learning algorithms Supervised - Prediction R-square RMS MAE MAPE Supervised- Classification Confusion Matrix ROC Curves Evaluation framework 62
  57. 57. 63 • 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 − ⁄∑"8) 9 (𝑦" − ;𝑦"), (𝑛 − 𝑝 − 1) ∑"8) 9 𝑦" − ?𝑦" , /(𝑛 − 1) • MAE or MAD (mean absolute error/deviation) gives the magnitude of the average absolute error 𝑀𝐴𝐸 = ∑"8) 9 𝑒" 𝑛 Prediction Accuracy Measures
  58. 58. 64 ▫ MAPE (mean absolute percentage error) gives a percentage score of how predictions deviate on average 𝑀𝐴𝑃𝐸 = ∑"8) 9 𝑒"/𝑦" 𝑛 ×100% • RMSE (root-mean-squared error) is computed on the training and validation data 𝑅𝑀𝑆𝐸 = 1/𝑛 H "8) 9 𝑒" , Prediction Accuracy Measures
  59. 59. 65 1. Data 2. Goals 3. Machine learning algorithms 4. Process 5. Performance Evaluation Recap
  60. 60. 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
  61. 61. #Disrupt19 Sentiment Analysis Using Natural Language Processing in Finance
  62. 62. • What is Sentiment Analysis? • The Case study Setup • Design Choices • The Pipeline • Demo #Disrupt19 Agenda
  63. 63. 69 What is NLP ? AI Linguistics Computer Science
  64. 64. 70 • 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
  65. 65. 71 NLP in Finance
  66. 66. 72 • The process of computationally identifying and categorizing opinions expressed in a piece of text, especially in order to determine whether the writer's attitude towards a particular topic, product, etc. is positive, negative, or neutral. Sentiment Analysis #Disrupt19
  67. 67. 73 • Understanding sentiments in Earnings call transcripts Goal 73
  68. 68. 74 • Interpreting emotions • Labeling data Options • APIs • Human Insight • Expert Knowledge • Build your own Challenges
  69. 69. 75 NLP pipeline Data Ingestion from Edgar Pre-Processing Invoking APIs to label data Compare APIs Build a new model for sentiment Analysis Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 • Amazon Comprehend API • Google API • Watson API • Azure API
  70. 70. 76 www.QuSandbox.com
  71. 71. 77 Agenda – Day 3
  72. 72. 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. 78

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