Machine Learning and AI has revolutionized Finance! In the last five years, innovations in computing, technology and business models have created multiple products and services in Fintech prompting organizations to prioritize their data and AI strategies. What will 2021 bring and how should you prepare for it? Join Sri Krishnamurthy,CFA as we kickoff the QuantUniversity’s Winter school 2021. We will introduce you to the upcoming programs and have a masterclass on 10 innovations in AI and ML you need to know in 2021!

1. QU Winter School 2021 Kickoff
+
10 things about ML you need to know in 2021
2021 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.quantuniversity.com
Jan 27th 2021

2. 2
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
“ML in Finance: A pragmatic approach”
• 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

3. 3
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 Audit and
Experimentation

4. 1. Winter school 2021
2. Key trends in AI, Machine Learning & Fintech
3. 10 things you need to know in 2021
4. Demos
Agenda

5. QuantUniversity Winter School 2021

6. 6
• Summer School 2020
Recap 2020

7. 7
• Fall School 2020
Recap 2020

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• 13+ countries
• 2400+ participants!
• Participants from FED, PWC, Goldman Sachs, OCC, E&Y, Deloitte, Genpact, BNP-
Paribas, Fidelity, State Street, Discover, Charles Schwab, etc. etc.
• Participant profile:
▫ Data Scientists
▫ Quants
▫ Risk Managers
▫ Consultants
▫ Regulators
▫ Students
Some numbers!

9. https://quwinterschool.splashthat.com/

10. 10
Guest speakers

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12. 12
Visit us at
www.quantuniversity.com to
know more!
• Use Code “QuAffiliate” for a
$100 off all courses
Join us!

13. AI and Machine Learning in Finance

14. 14
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

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Scientists are disrupting the way we live!
Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/

16. 16
Interest in Machine learning continues to grow
https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf

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MACHINE LEARNING AND AI IS REVOLUTIONIZING FINANCE

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• 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
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1. https://en.wikipedia.org/wiki/Machine_learning
2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf

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https://www.federalreserve.gov/supervisionreg/srletters/sr
1107a1.pdf
Source:
A model consists of three components:
an information input component, which
delivers assumptions and data to the model;
a processing component, which transforms
inputs into estimates;
a reporting component, which translates the
estimates into useful business information

20. https://landscape.lfai.foundation/

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Machine Learning & AI in finance: A paradigm shift
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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

22. 22
The Virtuous Circle of
Machine Learning and AI
22
Smart
Algorithms
Hardware
Data

23. 23
The rise of Big Data and Data Science
23
Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

24. 24
Smart Algorithms
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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

25. 25
Hardware
Speed up calculations with
1000s of processors
Scale computations with
infinite compute power

26. 26
“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

27. 27
http://www.analyticscertificate.com/fintech/

28. 28
http://www.analyticscertificate.com/fintech/

29. 29
http://www.analyticscertificate.com/fintech/

30. 30
http://www.analyticscertificate.com/fintech/

31. 31
Source: https://www.cbinsights.com/research/artificial-intelligence-top-startups/

34. 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/Quants
Software/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
&
Decision
Makers

36. 36
http://www.asimovinstitute.org/neural-network-zoo/

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42. 42

44. 44
Race to the cloud

46. 46
ML as a service

48. 48
AutoML ecosystem grows
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50. 50
Heated discussions on AI-Bias, Fairness, Ethics
50

51. 51
Regulation is coming!
51
https://www.housingwire.com/articles/fed-considers-
regulating-ai-used-by-financial-institutions/

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55. 55
Synthetic VIX generation
Extreme scenario generation

56. 56
To try the QuSandbox and QuSynthesize or any of
the other demos,
Email us at info@qusandbox.com
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57. https://quwinterschool.splashthat.com/

58. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Contact
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