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Presented by:Roderick Powell, Director, KPMGPatrick Rogers, CMO, AyasdiMukund Ramachandran, Data Scientist, Ayasdi
October 27, 2015
GARP Webcast
Effective Risk Models using Machine Intelligence
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Roderick Powell, FRM, is currently a Director in the Market and Treasury Risk practice at KPMG, LLP. He assists clients in validating and building models to price complex financial instruments and assess financial risk. Prior to joining KPMG, Powell was a Senior Capital Markets Specialist at the Federal Reserve Bank of Atlanta. While at the FED, he was responsible for examining models used to measure financial risk in banking and trading books, as well as reviewing CCAR Stress Test results. Powell previously worked as an independent consultant where he was engaged to derive the fair value of Lehman Brothers’ trading portfolio for a high-profile court case. Powell has held risk positions at Bank of America, ABN AMRO/LaSalle Bank, and FBOP Corporation.
Powell earned a B.S. degree in Finance and an MBA from Florida State University. He holds the designation of Certified Financial Risk Manager from the Global Association of Risk Professionals. He is the co-Director of the Atlanta Chapter of GARP.
Roderick Powell, KPMG
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Patrick Rogers leads the marketing function at Ayasdi. He spearheads the effort to drive awareness and adoption of Ayasdi’s revolutionary approach to data analysis and insight discovery. His expertise lies in translating compelling, new technology into real-world business solutions, and scaling growth of new use cases that provide outstanding benefits to customers and their clients. Patrick spent his career managing marketing and business development functions in high-growth businesses at NetApp, Scale8 and Hewlett-Packard. Most recently, he was VP Solutions and Integrations at NetApp, focused on innovative new marketing and selling approaches, including FlexPod, a joint solution effort between NetApp and Cisco that reached a market-leading position in virtualized, converged infrastructure. Previously, he led the product, alliance and solution marketing function at NetApp during the period when the company grew from $1B to $5B in revenues. He also led the HP9000 Unix/RISC Server marketing function at HP during the period the business reached $3B in annual revenues. Patrick holds an M.B.A. from Harvard University, and an M.S. and B.S. from the Massachusetts Institute of Technology.
Patrick Rogers, Ayasdi
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Mukund Ramachandran, Ayasdi
Mukund Ramachandran is a data scientist with Ayasdi focusing on the financial services and healthcare industries. Mukund joined Ayasdi from Supplyframe, a venture-backed startup transforming the electronic components supply chain model. Prior to Suppyframe, Mukund worked at Panorama Capital, the successor to JP Morgan Partner’s venture fund. Mukund began his career with Credit Suisse as an investment banking analyst in the technology M&A practice in the San Francisco office. Mukund earned his undergraduate degree in applied mathematics from the University of California at Berkeley. He went onto Boston University where he earned a Masters in Electrical and Computer Engineering where he focused his coursework on applying machine learning techniques to complex image processing problems.
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Risk Model Requirements
Speed DefensibilityAccuracy
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Complexity is the Challenge
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c2,700
variables
# of possible models created
from the Dataset exceeds two
trillion
# of possible models created from the Dataset next year
after it grows another 40% exceeds eight
trillion
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Challenges with Risk Models
Quants ConventionalMachine Learning
Laborious, iterative process
Black-box models, with limited business input
Risk of over-fitting
Difficult to justify to regulators
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A Man-Machine Workflow
Algorithms +Compute
Group of Variables
Data Statistical Tests
ModelsBusiness Input
Business Validation
Variable Selection
Model Selection
Variable Identification
Model Identification
Machines
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Introducing Machine Intelligence
+ +
Topological Data Analysis
ScalableCompute
Machine Learning, Geometric + Statistical Algorithms
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Comparing Approaches
Public + Internal
Variables
Conventional Methodology
Select a Subset of Features
Transform the Selected Features Prototype Models Solicit Business
InputValidate Models
Machine Intelligence Methodology
Public + Internal
Variables
Transform all the Available Features
Automatically Create a Similarity Map
Solicit Business Input to Select
Relevant Features
Use the Selected Features to
Create ModelsValidate Models
IterateWeeks, Months, Quarters
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Feature Engineering
Fed + InternalMacro Variables
~300Transforms
~900Lagged ~2700
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Rapidly identify highly correlated variables
Summary
Create simple, accurate, defensible
modelsIncorporate business
logic
Transparent Review
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Go beyond modeling for regulatory stress
tests
Beyond Revenue Forecasting and CCAR
Insights that drive business value -
beyond the mandates
Use the framework to forecast other risks
and returns
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Machine Intelligence Simulation
Watch a demonstration - Link
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Q&A
Q&A
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About GARP | The Global Association of Risk Professionals (GARP) is a not-for-profit organization dedicated to the risk management profession through education, training and the promotion of best practices globally. With a membership of over 150,000 individuals, GARP is the only worldwide organization offering comprehensive risk management certification, training and educational programs from board-level to entry-level. To learn more about GARP, please visit www.garp.org.
Creating a culture of risk awareness®
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www.garp.org
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