machine learning and ai in risk management
TRANSCRIPT
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
www.analyticscertificate.com
• 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 KrishnamurthyFounder and CEO
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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
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• 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
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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
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• “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
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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
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The Virtuous Circle of Machine Learning and AI
Smart
Algorithms
Hardware
Data
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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
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Smarter AlgorithmsParallel 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
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Hardware
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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
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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
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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”
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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!
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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
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Claim:• Machine Learning and AI will replace
humans in most applicationsCaution:• 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
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A framework for evaluating your organization’s appetite for AI and machine learning
Source: http://www.fsb.org/wp-content/uploads/P011117.pdf
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Regulators are catching up
http://www.fsb.org/wp-content/uploads/P011117.pdf
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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
Thank you!
Sri Krishnamurthy, CFA, CAPFounder and Chief Data Scientist
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.comwww.analyticscertificate.com
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not bedistributed or used in any other publication without the prior written consent of QuantUniversity LLC.
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