effective risk models using machine intelligence

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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

12Company Confidential & Proprietary

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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

http://www.ayasdi.com/resources/creating-effective-revenue-forecast-models-ccar/ccar-demo/?utm_source=webinar&utm_medium=deck&utm_campaign=garp&utm_content=ccar

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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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http://www.garp.org/

effective risk models using machine intelligence

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