Big Data Beyond The Hype - Amazon S3 · Data Science allows us to make decisions based on...

How Data Science Can Help Finance Professionals Make Better Decisions

Big Data Beyond The Hype

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.

© Copyright 2016 Cognitir LLC. All Rights Reserved.• Authors: David Haber and Neal Kumar• www.cognitir.com

2Copyright 2016 Cognitir LLC. All Rights Reserved.

Who We Are

David HaberCo-Founder & CPO

Neal Kumar, CFACo-Founder & CEO

@cognitir Cognitir Cognitir


Digital “Exhaust” Data

2.5 quintillion bytesEvery 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


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


Big Data

Big Data refers to datasets that are too large for traditional systems to capture,

store, manage, and analyze.


Relevance



Relevance (cont’d)



Data Science

Data Science is the art of extracting useful knowledge from data.

DataScience

Statistics

MachineLearning

SoftwareEngineering

DomainExpertise

DataEngineering

Mathematics


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 andintuition.

– Or, using DDD, she could base her selection based on an analysis of past successful companies. Source: Data Science for Business, Provost & Fawcett


Data Science Methods

Source: Data Science for Business, Provost & Fawcett


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.



Classification involves predicting a categorical (often binary) target value based on historical data.

What is the likelihood that a new customer will default on his loan?

Source: Data Science for Business, Provost & Fawcett



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?


Clustering attempts to find natural subgroups in our data.

Can we find similarities/patterns in historical stock data?


Data Step 1 Iteration 1, Step 2a

Iteration 1, Step 2b Iteration 2, Step 2a Final Results



Co-occurrence grouping attempts to find associations between entities based on transactions involving them.

Which financial products are commonly purchased together?



Similarity matching attempts to identify similar individuals/entities based on knowndata.– 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?



Ensemble Learning

Neural NetworksNatural Language Processing

Sentiment Analysis

Time Series Analysis

Genetic Algorithms

Optimization

Crowdsourcing

A/B Testing

Dimensionality Reduction


Interlude

Demo


Applications in Finance, Fintech, and Economics

Use of credit decision technology to provide access to credit for people with little or no credit history.

20

“Banking the Underbanked”

Source: kreditech.com

Copyright 2016 Cognitir LLC. All Rights Reserved.

Applications in Finance, Fintech, and Economics (cont’d)

Analysis of massive amounts of data to create unparalleledinvestment strategies.

21

Open invite hack sessions where people around the world can submit trading algorithms and profit share if such algorithms are successful



22

Angel and VC investors are utilizing data science to help improve odds that they are investing in winning ideas.

Robo-advisors are online wealth management services that provide automated, algorithm-based portfolio management advice.



23

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.


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



Capturing the Full Potential of Big Data



Thank you!

@cognitir Cognitir Cognitir

www.cognitir.com

[email protected]


Big Data Beyond The Hype - Amazon S3 · Data Science allows us to make decisions based on...

Documents

Transcript of Big Data Beyond The Hype - Amazon S3 · Data Science allows us to make decisions based on...