This document outlines an agenda for a master class on AI and machine learning for financial professionals presented by Sri Krishnamurthy. The speaker bio introduces Sri as an experienced financial analyst and consultant who has taught at several universities. The agenda includes an overview of key trends in AI and machine learning, a machine learning primer, and case studies. The document provides background on QuantUniversity and concludes by thanking attendees.

1. A Master Class in AI and Machine Learning
for Financial Professionals
2019 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.analyticscertificate.com
08/06/2019
Online Master Class

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
“Financial Modeling: A case study approach”
published by Wiley
• Teaches Analytics in the Babson College MBA
program and at Northeastern University,
Boston
• Reviewer: Journal of Asset Management
Sri Krishnamurthy
Founder and CEO
QuantUniversity

3. 3
About www.QuantUniversity.com
• 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 Enablement
in the Enterprise

4. • Key trends in AI and machine learning
• Machine Learning in 20 minutes
• Case studies
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 & AI in finance: A paradigm shift
11
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

12. 12
CFA Institute has adopted Fintech and AI content in its curriculum
Ref: https://www.cfainstitute.org/-/media/documents/support/programs/cfa/cfa-program-level-iii-fintech-in-investment-management.ashx

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

18. 18
• 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
Definitions: Machine Learning and AI
18
1. https://en.wikipedia.org/wiki/Machine_learning
2. Figure Source: http://www.fsb.org/wp-content/uploads/P011117.pdf

19. 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

20. 20
1. Data
2. Goals
3. Machine learning algorithms
4. Process
5. Performance evaluation
Key steps involved

22. 22
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

23. 23
Variables
A variable could be:
▫ Categorical
– Yes/No flags
– AAA,BB ratings for bonds
▫ Numerical
– 35 mpg
– $170K salary

24. 24
Longitudinal
▫ Observations are dependent
▫ Temporal-continuity is required
Cross-sectional
▫ Observations are independent
Datasets

25. 25
Data
Cross
sectional
Numerical Categorical
Longitudinal
Numerical
Summary
25

27. 27
• 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
27

28. 28
• 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
28

29. 29
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
29

31. 31
Machine Learning
Unsupervised Supervised
Reinforcement Semi-Supervised
Machine Learning

32. 32
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
32

33. 33
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
33
x1,x2,x3… Model F(X) y

34. 34
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
34
Obs1,
Obs2,Obs3
etc.
Model
Obs1- Class 1
Obs2- Class 2
Obs3- Class 1

35. 35
• Parametric models
▫ Assume some functional form
▫ Fit coefficients
• Examples : Linear Regression, Neural Networks
Supervised Learning models - Prediction
35
𝑌 = 𝛽' + 𝛽) 𝑋)
Linear Regression Model Neural network Model

36. 36
• Non-Parametric models
▫ No functional form assumed
• Examples : K-nearest neighbors, Decision Trees
Supervised Learning models
36
K-nearest neighbor Model Decision tree Model

37. 37
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
37

38. 38
Machine Learning movers and shakers
Deep
Learning
Automatic
Machine
Learning
Ensemble
Learning
Natural
Language
Processing

39. 39
http://www.asimovinstitute.org/neural-network-zoo/

41. 41
The Process
41
Data
ingestion
Data
cleansing
Feature
engineering
Training
and testing
Model
building
Model
selection

42. 42
• 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
42

43. 43
Data
Training
80%
Testing
20%
Training the model
43

45. 45
Evaluating
Machine learning
algorithms
Supervised -
Prediction
R-square RMS MAE MAPE
Supervised-
Classification
Confusion Matrix ROC Curves
Evaluation framework
45

46. 46
• 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 −
⁄∑"8)
9
(𝑦" − ;𝑦"), (𝑛 − 𝑝 − 1)
∑"8)
9
𝑦" − ?𝑦"
, /(𝑛 − 1)
• MAE or MAD (mean absolute error/deviation) gives the magnitude of the
average absolute error
𝑀𝐴𝐸 =
∑"8)
9
𝑒"
𝑛
Prediction Accuracy Measures

47. 47
▫ MAPE (mean absolute percentage error) gives a percentage score of
how predictions deviate on average
𝑀𝐴𝑃𝐸 =
∑"8)
9
𝑒"/𝑦"
𝑛
×100%
• RMSE (root-mean-squared error) is computed on the training and
validation data
𝑅𝑀𝑆𝐸 = 1/𝑛 H
"8)
9
𝑒"
,
Prediction Accuracy Measures

48. 48
1. Data
2. Goals
3. Machine learning algorithms
4. Process
5. Performance Evaluation
Recap

49. 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

50. #Disrupt19
Credit Risk Decision Making Using Lending Club Data

51. 51
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

52. 52
Credit risk in consumer credit
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 ?

53. 53
Credit assessment in consumer credit
History:
• Gut feel
• Social network
• Communities and influence
Traditional:
• Scoring mechanisms through credit bureaus
• Bank assessments through business rules
Newer approaches:
• Peer-to-Peer lending
• Prosper Market place

54. 54
The Data
54
https://www.kaggle.com/wendykan/lending-club-loan-data

55. 55
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

56. 56
56

57. 57
4-week online course in AI & ML in Finance
10/4/2019 to 10/25/2019– Livestream
1-day class in AI &ML in Finance
August 12th 2019 –New York & Livestream
Where do you go from here
https://cfa-sf.org/events/EventDetails.aspx?id=1258670&group=
https://www.cfany.org/event/machine-learning-and-ai-for-financial-professionals/

58. 58
1. Whitepapers at www.quantuniversity.com
2. https://blogs.cfainstitute.org/investor/tag/machine-learning/
3. https://techcrunch.com/
4. https://www.technologyreview.com/
5. https://www.bbc.com/timelines/zypd97h
6. https://www.bbc.com/timelines/zq376fr
Additional Reading

59. 59
www.QuSandbox.com

60. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Contact
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
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