1. The document provides an agenda for a presentation on machine learning and AI in finance. The presentation will cover key trends in AI, an introduction to machine learning concepts, and two case studies on interest rate prediction and synthetic data generation. 2. The speaker is introduced as an advisory consultant with experience in financial analytics and teaching AI/ML topics. He is the founder and CEO of QuantUniversity, a training platform for quantitative methods. 3. The presentation will provide an intuitive introduction to machine learning and AI concepts and discuss their growing impact and applications in finance industry such as fraud detection and arbitrage opportunities.

1. Machine Learning and AI
An intuitive Introduction
2020 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.qu.academy
Oct 27thth, 2020
Online

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
“Pragmatic AI and ML in Finance”
• 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
Experimentation

4. 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

5. AI and Machine Learning in Finance

6. 6
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

7. 7
Scientists are disrupting the way we live!
Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/

8. 8
Interest in Machine learning continues to grow
https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf

9. 9
MACHINE LEARNING AND AI IS REVOLUTIONIZING FINANCE

10. 10
Market impact at the speed of light!
10

11. 11
• 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
11
1. https://en.wikipedia.org/wiki/Machine_learning
2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf

12. 12
Machine Learning & AI in finance: A paradigm shift
12
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

13. 13
The Virtuous Circle of
Machine Learning and AI
13
Smart
Algorithms
Hardware
Data

14. 14
The rise of Big Data and Data Science
14
Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

15. 15
Smart Algorithms
15
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

16. 16
Hardware
Speed up calculations with
1000s of processors
Scale computations with
infinite compute power

17. 17
“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

18. 18
http://www.analyticscertificate.com/fintech/

19. 19
http://www.analyticscertificate.com/fintech/

20. 20
http://www.analyticscertificate.com/fintech/

21. 21
http://www.analyticscertificate.com/fintech/

22. 22
Source: https://www.cbinsights.com/research/artificial-intelligence-top-startups/

23. 23
• 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?

25. 25
Let’s get under the hood
25
Source: https://www.pikrepo.com/fcsda/yellow-hot-rod-car-with-hood-open

26. 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

27. 27

28. 28

29. 29
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

30. 30
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

31. 31
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”

32. 32
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!

33. 33
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

34. 34
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

35. #Disrupt19
Alternative investments: Interest rate predication for Peer-to-Peer Market places using ML
techniques

36. 36
How Lending club works?
https://www.lendingclub.com/public/how-peer-lending-
works.action

37. 37
The Data
37
https://www.kaggle.com/wendykan/lending-club-loan-data

38. 38
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

39. 39
39

40. #Disrupt19
Synthetic VIX data generation using Neural Networks

41. 41
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

42. 42
Missing values
• Missing at random
• Missing sequences
• Need data to fill frames
Challenges with real datasets

43. 43
• Access
▫ Hard to find
▫ Rare class problems
▫ Privacy concerns
making it difficult to
share
Challenges with real datasets

44. 44
Imbalanced
• Need more samples of rare
class
• Need proxies for data points
that were not observed or
recorded
Challenges with real datasets

45. 45
Labels
• Human labeling is hard
• Synthetic label generators
Challenges with real datasets

46. 46
GAN
https://developers.google.com/machine-
learning/gan/gan_structure

47. 47

48. 48
Demo: Synthetic VIX generation
Extreme scenario generation

49. Register at
https://qufallschool.splashthat.com/
Classes start
Oct 2020
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50. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.qu.academy
Contact
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