Big Data Beyond The Hype. How Data Science Can Help Finance Professionals Make Better Decisions

Transcription

1 Big Data Beyond The Hype How Data Science Can Help Finance Professionals Make Better Decisions

2 Terms and Conditions Distributing, copying, sharing, duplicating, and/or altering this file in any way is prohibited without the expressed written consent of Cognitir LLC. Authors: David Haber and Neal Kumar 2

3 Who We Are David Haber Co-Founder & CPO Neal Kumar, CFA Co-Founder & Cognitir Cognitir 3

4 Digital Exhaust Data 2.5 quintillion bytes Every day, we create 2.5 quintillion bytes of data so much that 90% of the data in the world today has been created in the last two years alone. Source: IBM 4

5 Digital Exhaust Data (cont d) In fact, we are generating so much data that it s physically impossible to store it all. Financial services sectors, including securities and investment services and banking, have the most digital data stored per firm on average. Source: McKinsey Global Institute 5

6 Big Data Big Data refers to datasets that are too large for traditional systems to capture, store, manage, and analyze. 6

7 Relevance Source: McKinsey Global Institute 7

8 Relevance (cont d) Source: McKinsey Global Institute 8

9 Data Science Data Science is the art of extracting useful knowledge from data. Statistics Mathematics Data Engineering Data Science Machine Learning Software Engineering Domain Expertise 9

10 How can Data Science help us make better decisions? Data Science allows us to make decisions based on historical data rather than on a hunch. A traditional venture capitalist could select investments based on her experience and intuition. Or, using DDD, she could base her selection based on an analysis of past successful companies. Source: Data Science for Business, Provost & Fawcett 10

11 Data Science Methods Source: Data Science for Business, Provost & Fawcett 11

12 Data Science Methods (cont d) Methods to learn a model generally fall into two groups: In supervised learning, we want to learn relationships between input (features) and output variables (target). If we don t have a specific target, the learning problem is called unsupervised. 12

13 Data Science Methods (cont d) Classification involves predicting a categorical (often binary) target value based on historical data. Source: Data Science for Business, Provost & Fawcett What is the likelihood that a new customer will default on his loan? 13

14 Data Science Methods (cont d) Regression involves predicting numeric/continuous target values based on historical data. If we target customers x with new financial products y, how much will our sales likely increase? 14

15 Data Science Methods (cont d) Clustering attempts to find natural subgroups in our data. Data Step 1 Iteration 1, Step 2a Iteration 1, Step 2b Iteration 2, Step 2a Final Results Can we find similarities/patterns in historical stock data? 15

16 Data Science Methods (cont d) Co-occurrence grouping attempts to find associations between entities based on transactions involving them. Which financial products are commonly purchased together? 16

17 Data Science Methods (cont d) Similarity matching attempts to identify similar individuals/entities based on known data. Which companies are similar to our best business customers so that we can focus our sales resources on the best opportunities? Profiling attempts to characterize the typical behavior of an individual, group, or population. What are the characteristics of businesses that are considered our best customers? What are the characteristics of those who will most likely default on loans? 17

18 Data Science Methods (cont d) Natural Language Processing Neural Networks Sentiment Analysis Ensemble Learning Time Series Analysis Genetic Algorithms Optimization Dimensionality Reduction Crowdsourcing A/B Testing 18

19 Interlude Demo 19

20 Applications in Finance, Fintech, and Economics Use of credit decision technology to provide access to credit for people with little or no credit history. Banking the Underbanked Source: kreditech.com 20

21 Applications in Finance, Fintech, and Economics (cont d) Analysis of massive amounts of data to create unparalleled investment strategies. Open invite hack sessions where people around the world can submit trading algorithms and profit share if such algorithms are successful 21

22 Applications in Finance, Fintech, and Economics (cont d) Robo-advisors are online wealth management services that provide automated, algorithmbased portfolio management advice. Angel and VC investors are utilizing data science to help improve odds that they are investing in winning ideas. 22

23 Applications in Finance, Fintech, and Economics (cont d) FP&A and PE Portfolio companies are using data science to boost top lines and improve efficiency. Economic consulting firms and macroeconomic forecasting teams within companies and financial institutions use data science to help predict leading economic indicators. 23

24 Applications in Finance, Fintech, and Economics (cont d) Other select applications of data science to finance, fintech, and economics include: Financial product sales: New products to existing customers New customers based on profiles of existing customers Portfolio Risk Management VaR Bitcoin / Blockchain Cloud-based mining Corporate Finance Cost of capital sensitivities (e.g., Beta) Selection of comparable companies for relative valuation Strategic alternatives event effects prediction 24

25 Capturing the Full Potential of Big Data Source: McKinsey Global Institute 25

26 Thank you! Cognitir Cognitir 26