Artificial intelligent systems in finance have exploded over the last few years. Many institutions are struggling to leverage these new AI systems and machine learning approaches to risk management. This is particularly true for applications to risk models that are subject to regulatory scrutiny where transparency limits applications of these new approaches. Co-sponsored with PRMIA (Professional Risk Managers’ International Association), this session will provide an overview of the current state of applied machine learning and artificial intelligence for risk modeling and how it can be applied for monitoring risk and building new risk models.

1. Location:
QU-MathWorks-PRMIA Webinar
12/06/2017
Machine Learning and AI in Risk Management
2017 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.analyticscertificate.com

2. • Founder of QuantUniversity LLC. and
www.analyticscertificate.com
• Advisory and Consultancy for Financial Analytics
• Prior Experience at MathWorks, Citigroup and
Endeca and 25+ financial services and energy
customers.
• Regular Columnist for the Wilmott Magazine
• Author of forthcoming book
“Financial Modeling: A case study approach”
published by Wiley
• Charted Financial Analyst and Certified Analytics
Professional
• Teaches Analytics in the Babson College MBA
program and at Northeastern University, Boston
Sri Krishnamurthy
Founder and CEO
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3. 3
What’s it like to be a risk manager in the age of Machine
Learning and AI?
Source: https://imgs.xkcd.com/comics/machine_learning.png
Risk Manager
Trader/Quant

4. 4
• Machine Learning and AI in Finance
▫ A quick introduction
• Machine Learning and AI: A practitioner’s perspective
▫ 5 things every Risk manager should know about
Agenda

5. 5
AI and Machine Learning in the News
https://www.economist.com/news/finance-and-economics/21722685-fields-
trading-credit-assessment-fraud-prevention-machine-learning
https://www.udacity.com/course/machine-learning-for-trading--ud501
https://www.forbes.com/sites/louiscolumbus/2017/10/23/machine-
learnings-greatest-potential-is-driving-revenue-in-the-
enterprise/#3fd4c2da41db
https://www.cnbc.com/2017/09/28/man-group-
one-of-worlds-largest-funds-moves-into-machine-
learning.html

6. 6
• “AI is the theory and development of computer systems able to
perform tasks that traditionally have required human intelligence.
• AI is a broad field, of which ‘machine learning’ is a sub-category”
What is Machine Learning and AI?
Source: http://www.fsb.org/wp-content/uploads/P011117.pdf

7. 7
Machine Learning & AI in finance – A paradigm shift
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

8. 8
The Virtuous Circle of Machine Learning and AI
Smart
Algorithms
Hardware
Data

9. 9
The rise of Big Data and Data Science
Image Source: http://www.ibmbigdatahub.com/sites/default/files/infographic_file/4-Vs-of-big-data.jpg

10. 10
Smarter Algorithms
Parallel and 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

11. 11
Hardware

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

13. 13
http://www.analyticscertificate.com/fintech/

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

15. 15
http://www.analyticscertificate.com/fintech/

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17. 17
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

18. 18
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

19. 19
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”

20. 20
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!

21. 21
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

22. 22
Claim:
• Machine Learning and AI will replace
humans in most applications
Caution:
• Beware of the hype!
• Just because it worked some times
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. Are we there yet?
https://www.bloomberg.com/news/articles/2017-10-20/automation-
starts-to-sweep-wall-street-with-tons-of-glitches

23. 23
A framework for evaluating your organization’s appetite for AI
and machine learning
Source: http://www.fsb.org/wp-content/uploads/P011117.pdf

24. 24
Regulators are catching up
http://www.fsb.org/wp-content/uploads/P011117.pdf

25. 25
About us:
• Data Science, Quant Finance and
Machine Learning Advisory
• Trained more than 1000 students in
Quantitative methods, Data Science
and Big Data Technologies using
MATLAB, Python and R
• Programs
▫ Analytics Certificate Program
▫ Fintech programs
• Platform

26. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and Chief Data Scientist
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
www.analyticscertificate.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.
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