Learn how artificial intelligence (AI) and machine learning are revolutionizing financial services — this course will introduce key concepts and illustrate the role of machine learning, data science techniques, and AI through examples and case studies from the investment industry. The presentation uses simple mathematics and basic statistics to provide an intuitive understanding of machine learning, as used by financial firms, to augment traditional investment decision making. This overview session offers a tour of machine learning and AI methods, examining case studies to understand the technology companies, data vendors, banks, and fintech startups that are the key players in trading and investment management. Practical examples and case studies will help participants understand key machine learning methodologies, choose an algorithm for a specific goal, and recognize when to use machine learning and AI techniques

1. MACHINE LEARNING AND AI:
AN INTUITIVE INTRODUCTION
29 April 2020, 9:00 am - 11:00 am EDT
Sri Krishnamurthy, CFA
President
QuantUniversity
Richard Fernand, Moderator
Senior Director, Global Content, Professional Learning
CFA Institute

2. Use Q&A to submit questions for the presenters
Use CHAT to share comments and to see
what others are saying. You can also select
who you would like to send the message to
by clicking on the drop down next to To:.

3. Machine Learning and AI: An intuitive Introduction
Part 1
2020 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.quantuniversity.com
04/29/2020
CFA Institute - Online

4. 4
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
“The Model-Driven Enterprise”
• 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

5. 5
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 Exploration
and Experimentation

6. 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

7. AI and Machine Learning in Finance

8. 8
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

9. 9
Scientists are disrupting the way we live!
Source: https://www.ladn.eu/tech-a-suivre/mobilite-2030-vehicules-volants-open-data/

10. 10
Interest in Machine learning continues to grow
https://www.wipo.int/edocs/pubdocs/en/wipo_pub_1055.pdf

11. 11
MACHINE LEARNING AND AI IS REVOLUTIONIZING FINANCE

12. 12
Market impact at the speed of light!
12

13. 13
• 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
13
1. https://en.wikipedia.org/wiki/Machine_learning
2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf

14. 14
Machine Learning & AI in finance: A paradigm shift
14
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

15. 15
The Virtuous Circle of
Machine Learning and AI
15
Smart
Algorithms
Hardware
Data

16. 16
The rise of Big Data and Data Science
16
Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

17. 17
Smart Algorithms
17
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

18. 18
Hardware
Speed up calculations with
1000s of processors
Scale computations with
infinite compute power

19. 19
“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

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

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

22. 22
http://www.analyticscertificate.com/fintech/

23. 23
http://www.analyticscertificate.com/fintech/

24. 24

25. Risk Systems That Read®
• Northfield uses machine learning based analysis of news text
to describe how current conditions in financial markets are
different than usual.
• Typically, over 8000 articles per day containing more than
20,000 “topics” (companies, industries, countries) are
processed.
• The nature and magnitudes of these difference are used to
revise expectations of financial market risks for all global
equities and credit instruments on a daily basis.

26. 26
1. Leveraging large and diverse datasets for
Investment decision making at J.P. Morgan1
2. Improving Quantitative investing at AQR2
3. Using Sandboxes and labs to further innovation
in fintech at Fidelity3
4. Use of AI and ML increasing in ssset
management from idea generation to execution -
Wells Fargo4
Additional Use cases
1. https://www.jpmorgan.com/global/cib/research/investment-decisions-using-machine-learning-ai
2. https://www.aqr.com/Learning-Center/Machine-Learning
3. https://www.fidelitylabs.com/
4. https://www08.wellsfargomedia.com/assets/pdf/personal/investing/investment-institute/IG_Machines_Are_Coming_ADA.pdf

27. 27
Source: https://www.cbinsights.com/research/artificial-intelligence-top-startups/

28. 28
• 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?

29. #Disrupt19

31. 31
Let’s get under the hood
31
Source: https://www.pikrepo.com/fcsda/yellow-hot-rod-car-with-hood-open

32. 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

33. 33
1. Data
2. Goals
3. Machine learning algorithms
4. Process
5. Performance evaluation
Key steps involved

35. 35
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

36. 36
Variables
A variable could be:
▫ Categorical
– Yes/No flags
– AAA,BB ratings for bonds
▫ Numerical
– 35 mpg
– $170K salary

37. 37
Longitudinal
▫ Observations are dependent
▫ Temporal-continuity is required
Cross-sectional
▫ Observations are independent
Datasets

38. 38
Data
Cross
sectional
Numerical Categorical
Longitudinal
Numerical
Summary
38

40. 40
• 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
40

41. 41
• 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
41

42. 42
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
42

44. 44
Machine Learning
Unsupervised Supervised
Reinforcement Semi-Supervised
Machine Learning

45. 45
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
45

46. 46
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
46
x1,x2,x3… Model F(X) y

47. 47
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
47
Obs1,
Obs2,Obs3
etc.
Model
Obs1- Class 1
Obs2- Class 2
Obs3- Class 1

48. 48
• Parametric models
▫ Assume some functional form
▫ Fit coefficients
• Examples : Linear Regression, Neural Networks
Supervised Learning models - Prediction
48
𝑌 = 𝛽! + 𝛽" 𝑋"
Linear Regression Model Neural network Model

49. 49
• Non-Parametric models
▫ No functional form assumed
• Examples : K-nearest neighbors, Decision Trees
Supervised Learning models
49
K-nearest neighbor Model Decision tree Model

50. 50
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
50

51. 51
Machine Learning movers and shakers
Deep
Learning
Automatic
Machine
Learning
Ensemble
Learning
Natural
Language
Processing
Data Robot
H20.ai
Autosklearn
autokkeras
Tensorflow
Pytorch
NLTK
HuggingFace
Bagging
Boosting
DNN
CNN
LSTM
GAN

52. 52
http://www.asimovinstitute.org/neural-network-zoo/

54. 54
The Process
54
Data
ingestion
Data
cleansing
Feature
engineering
Training
and testing
Model
building
Model
selection

55. 55
• 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
55

56. 56
Data
Training
80%
Testing
20%
Training the model
56

58. 58
Evaluating
Machine learning
algorithms
Supervised -
Prediction
R-square RMS MAE MAPE
Supervised-
Classification
Confusion Matrix ROC Curves
Evaluation framework
58

59. 59
• 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 −
⁄∑"#$
%
(𝑦" − 0𝑦")! (𝑛 − 𝑝 − 1)
∑"#$
%
𝑦" − 4𝑦"
! /(𝑛 − 1)
• MAE or MAD (mean absolute error/deviation) gives the magnitude of the
average absolute error
𝑀𝐴𝐸 =
∑"#$
%
𝑒"
𝑛
Prediction Accuracy Measures

60. 60
▫ MAPE (mean absolute percentage error) gives a percentage score of
how predictions deviate on average
𝑀𝐴𝑃𝐸 =
∑!"#
$
𝑒!/𝑦!
𝑛
×100%
• RMSE (root-mean-squared error) is computed on the training and
validation data
𝑅𝑀𝑆𝐸 = 1/𝑛 2
!"#
$
𝑒!
%
Prediction Accuracy Measures

61. 61
1. Data
2. Goals
3. Machine learning algorithms
4. Process
5. Performance Evaluation
Recap

62. 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

63. 63

64. 64

65. 65
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

66. 66
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

67. 67
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”

68. 68
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!

69. 69
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

70. 70
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

71. #Disrupt19

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

73. 73
1. Case Intro
2. Data Exploration of the Credit risk data set
3. Problem Definition and Machine learning
4. Performance Evaluation
5. Deployment
Case study

74. 74
Credit decisions
Credit-scoring models and techniques assess the risk in
lending to customers.
Typical decisions:
• Grant credit/not to new applicants
• Increasing/Decreasing spending limits
• Increasing/Decreasing lending rates
• What new products can be given to existing applicants ?

75. 75
How Lending club works?
https://www.lendingclub.com/public/how-peer-lending-
works.action

76. 76
• How much should I expect as interest?
• Is my borrower credit worthy?
• How much interest would a similar borrower pay?
• What is the repayment and default rate for a similar borrower?
Investor’s big decisions

77. 77
The Data
77
https://www.kaggle.com/wendykan/lending-club-loan-data

78. 78
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

79. 79
79

80. #Disrupt19
Synthetic VIX data generation using Neural Networks

81. 81
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

82. 82
Missing values
• Missing at random
• Missing sequences
• Need data to fill frames
Challenges with real datasets

83. 83
• Access
▫ Hard to find
▫ Rare class problems
▫ Privacy concerns
making it difficult to
share
Challenges with real datasets

84. 84
Imbalanced
• Need more samples of rare
class
• Need proxies for data points
that were not observed or
recorded
Challenges with real datasets

85. 85
Labels
• Human labeling is hard
• Synthetic label generators
Challenges with real datasets

86. 86
VAE
https://arxiv.org/pdf/1808.06444.pdf

87. 87
GAN
https://developers.google.com/machine-
learning/gan/gan_structure

88. 88

89. 89
Demo 1 – Loan Data Synthesizer
(Anonymization + Data Augmentation)

90. 90
Demo 2: Synthetic Sales data generation
What-if & Scenario analysis

91. 91
Demo 3 : Synthetic VIX generation
Extreme scenario generation

92. 1. Key trends in AI, Machine Learning & Fintech
2. An intuitive introduction to AI and ML
3. Case study
▫ Building an interest rate predicator using ML techniques
▫ Synthetic data generation using Neural Networks
Part 1: Recap
92

93. 93
To try the QuSandbox and QuSynthesize or any of
the other demos,
Email us at info@qusandbox.com
93

94. 94
Sign up at:
https://www.cfainstitute.org/en/events/w
ebinars/machine-learning-and-ai-core-
methods-and-applications
Signup to Part 2 of this Master class!

95. Register at
https://mlinfinance.splashthat.com
Classes start
May 12th
95

96. #Disrupt19

97. Thank you!
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
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.
97