Revolution Confidential

R in the Financial Services Industry

June 6, 2013

Karl-Kuno KunzeNeil MillerAndrie De Vries

Breakfast Briefing

Revolution ConfidentialR in Financial Services

Welcome & Revolution

Neil Miller Managing Director, International Andrie de Vries Business Services Director, Europe Revolution Analytics

R in Financial Institutions

Karl-Kuno Kunze Managing Director Nagler & Company

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Revolution ConfidentialRevolution Analytics Corporate Overview & Quick Facts

Founded 2007

Office Locations Palo Alto (HQ), Seattle (Engineering)SingaporeLondon

CEO David Rich

Number of customers

200+

Investors • Northbridge Venture Partners• Intel Capital• Platform Vendor

Web site: • www.revolutionanalytics.com

Revolution – “Contender” The Forrester Wave™: Big DataPredictive Analytics Solutions, Q1 2013

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In the big data analytics context, speed and scale are critical drivers of success, and Revolution R delivers on both

Revolution R Enterprise is the leading commercial analytics platform based on the open source R statistical computing language

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Incredible graphics, visualization and flexiblestatistical analytics capabilities

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4500+ packages

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has some constraints for enterprise use

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Innovate for breakthroughs

&&

Scale & power your analytics

Deploy widely with confidence

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Revolution R Enterprise

ScaleRDistributed High Performance Architecture +

High Performance Big Data Analytics

packages

RevoRPerformance Enhanced Open Source R + Open Source R

packages

g pConnectR

High Speed Connectors

PlatformRDistributed Compute Contexts

DevelopRIntegrated Development

Environment

DeployRWeb Services

Revolution R EnterpriseHigh Performance, Multi-Platform Enterprise Analytics Platform

Revolution ConfidentialDistributedR and ScaleR processing handles big data and / or big analytics.

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Revolution ConfidentialIntegration Layer:DeployR makes R accessible

SeamlessBring the power of R to any web enabled application

SimpleLeverage common APIs including JS, Java, .NET

ScalableRobustly scale user and compute workloads

SecureManage enterprise security with LDAP & SSO

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R / Statistical Modeling Expert

DeployR

Data AnalysisData Analysis

Business IntelligenceBusiness Intelligence

Mobile Web AppsMobile Web Apps

Cloud / SaaSCloud / SaaS

DeploymentExpert

Revolution ConfidentialOn-Demand Analytics with DeployR

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Market Basket Analysis using Java Script and R enabled by DeployR

•User selection drives Java Script…

•which drives R script… •which drives Java Script to return to user data and graphics needed…

•…enabled by DeployR API’s

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Example: Allstate performance assessment of SAS, R, Hadoop, Revolution (October 2012)

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• Steve Yun, Principal Predictive Modeller at Allstate Research and Planning Centre benchmarked SAS, R and Hadoop. “Data is our competitive advantage”.

• Generalised Linear Model for 150 million observations of insurance data and 70 degrees of freedom.

Conclusion: • SAS works, but is slow. • The data is too big for open-source R, even on a very large server.• Hadoop is not a right fit • Revolution ScaleR gets the same results as SAS, but much faster and on cheaper kit

Software Platform Comments Time to fitSAS (current tool)

16-core Sun Server Proc GENMOD 5 hours

rmr / map-reduce

10-node (8 cores / node) Hadoopcluster

Lot of coding, prep and error investigation. Possible to improve time?

> 9 hours processing

Open source R 250-GB Server Full data set and sampling. Sampling quicker but not acceptable to business.

Impossible(> 3 days)

RevolutionScaleR

5-node (4 cores / node) LSF cluster

90 minutes to load full data set 5.7minutes

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“As things become more and more extreme, I need a model that can estimate my risk in a way to that enhances our confidence in our pricing and reserving. Modeling with Revolution R Enterprise gives me that.”

VP and Pricing Actuary, Jamie Botelho

Economic Capital Modeling

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1 day to 15 minutes100,000 years of simulationsPricing optimization increases financial health

Profile: 10-year-old reinsurer’s Actuarial Group systematically makes sound financial and pricing decisions in production system and completes ad hoc analysis.

Key Technology: Revolution R Enterprise replaced Excel; drives business rules in company production system

Outcomes: Ability to compensate for lack of historical data by simulating a wide variety and quantity of events and using advanced correlation techniques. Complete full day of work in 15 minutes

Bottom line: Improved financial health by managing risk and increasing pricing optimization

Revolution ConfidentialF100 Investment Co. Outlier & Error Detection

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Profile: Full-service global investment and securities management firm proved effectiveness of Revolution R Enterprise to detect potentially costly outliers and errors

Key Technology: Revolution R Enterprise using ScaleR Big Data Analytics capabilities

Analytic Approach – Exchange Rate Error Detection: ARIMA and VAR models used to define acceptable value changes using the prediction for the next value in a time series. Models trained using historical data.

“The models’ performance were impressive and few errors were missed.” VP, IT

Bottom line: new analytics paradigm for existing processes introduced, with potential for millions of dollars in cost avoidance

>65M end-of-day trades>8,500 variablesWeekly model re-training

Analytic Approach – Outlier Detection: Use historical data for each customer (>65M end-of-day trades and >8,500 variables) to build and train linear regression model to establish range of predicted values for customers’ trades so that actual trades can be analyzed for outliers.

“Using statistical analysis by customer delivers superior accuracy compared to rules-based analysis (such as analyzing largest 10% of trades), which fail over time as volumes or client behavior changes. Statistical models that can be retrained (e.g weekly) will account for changes and not fail over time.” VP, IT

Revolution ConfidentialQuantitative Research @ Global Investment Co.

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Profile: Full-service global investment and securities management firm’s IT team proved effectiveness of Revolution R Enterprise to detect potentially costly outliers and errors

Key Technology: Revolution R Enterprise using ScaleR Big Data and DeployR integrated with Siteminder, which provides a secure, transparent, centralized analytics center.

Analytic Approach – develop models that can be applied to real-world data to exploit market opportunities and successfully develop, back-test, and deploy quantitative and event-based trading and investment strategies to effectively manage risk.

Quants’ daily model updates deployed to 100’s of traders

Challenge - Quantitative Research Group had a decentralized modeling practice where quants used Excel, Python, Java, open source R, and other tools to develop models that informed daily trading. This environment posed risk to IP protection, model versioning, transparency.

Bottom Line - Powerful statistical analytics platform provides centralized, secure model repository guides hundreds of millions of dollars of transactions made by 100’s of traders.

Revolution ConfidentialInnovates to Outperform

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“One of the first R-based production deployments we rolled out tracks revenue flows among manufacturers and their suppliers. We combine public and proprietary data and apply graph analyses to get a clearer understanding of the likely performance of suppliers. These forecasts are more accurate than what could be developed with quarters-old public financial reports.”

- Sr. Quantitative Researcher, Tal Sasani

Profile: Publicly-traded, investment management company that includes the Livestrong family of funds. Revolution R Enterprise optimizes $8.5B portfolio of 22 funds.

Key Technology: Revolution R Enterprise replacing proprietary industry applications. Tableau front end for production analytics.

Outcomes: Battery of custom analytics now run overnight to inform morning work

Put R-based analytics into production

Bottom Line: Custom-built simulations, scenario analyses & financial stress tests improve confidence in forecasts and analysis, lifting the business

New data, more liftStrategy simulation & portfolio optimizationDays to overnight

Revolution ConfidentialOther Financial Services examples Op Risk: Conducting Monte Carlo simulations on 100,000 years of simulated data to measure

aggregate operational risk from 7 types of operational risk in accordance with BASEL II requirements

Mortgage loan default analysis and prediction in a Hadoop environment Moved from SAS = lower cost, better model uplift, better Hadoop integration

Credit Scoring in Database with Netezza: Increased Speed

Model Governance Issues: Model management through DeployR – changing analyst community and business user access via Qlikview, Excel, Python

Using Revolution to support SAS to analyse foreign trade transactions to identify anomalies: Better data exploration and visualisation

Control – “1600 SAS programmers and all the new guys coming in know R – now is the time to get my hands around R before it spins out of control with all these new R zealots coming on board”

IT Innovation – starting to use Hadoop. SAS too hard to write map reduce jobs Cross Platform – 500 Teradata appliances and 10 Netezza. Seamlessly deploy analysis across

their infrastructure

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Revolution ConfidentialHigh Performance R & Big Data Analytics Parallel External Memory Algorithms

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Data import – Delimited, Fixed, SAS, SPSS, OBDC

Variable creation & transformation

Recode variables Factor variables Missing value handling Sort Merge Split Aggregate by category

(means, sums)

Data import – Delimited, Fixed, SAS, SPSS, OBDC

Variable creation & transformation

Recode variables Factor variables Missing value handling Sort Merge Split Aggregate by category

(means, sums)

Min / Max Mean Median (approx.) Quantiles (approx.) Standard Deviation Variance Correlation Covariance Sum of Squares (cross product

matrix for set variables) Pairwise Cross tabs Risk Ratio & Odds Ratio Cross-Tabulation of Data

(standard tables & long form) Marginal Summaries of Cross

Tabulations

Min / Max Mean Median (approx.) Quantiles (approx.) Standard Deviation Variance Correlation Covariance Sum of Squares (cross product

matrix for set variables) Pairwise Cross tabs Risk Ratio & Odds Ratio Cross-Tabulation of Data

(standard tables & long form) Marginal Summaries of Cross

Tabulations

Chi Square Test Kendall Rank Correlation Fisher’s Exact Test Student’s t-Test

Chi Square Test Kendall Rank Correlation Fisher’s Exact Test Student’s t-Test

Data Prep, Distillation & Descriptive Analytics Data Prep, Distillation & Descriptive Analytics

Subsample (observations & variables)

Random Sampling

Subsample (observations & variables)

Random Sampling

R Data Step Statistical Tests

Sampling

Descriptive Statistics

Revolution ConfidentialHigh Performance R & Big Data Analytics Parallel External Memory Algorithms

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Sum of Squares (cross product matrix for set variables)

Multiple Linear Regression Generalized Linear Models (GLM)

- All exponential family distributions: binomial, Gaussian, inverse Gaussian, Poisson, Tweedie. Standard link functions including: cauchit, identity, log, logit, probit. User defined distributions & link functions.

Covariance & Correlation Matrices

Logistic Regression Classification & Regression Trees Predictions/scoring for models Residuals for all models

Sum of Squares (cross product matrix for set variables)

Multiple Linear Regression Generalized Linear Models (GLM)

- All exponential family distributions: binomial, Gaussian, inverse Gaussian, Poisson, Tweedie. Standard link functions including: cauchit, identity, log, logit, probit. User defined distributions & link functions.

Covariance & Correlation Matrices

Logistic Regression Classification & Regression Trees Predictions/scoring for models Residuals for all models

Histogram Line Plot Scatter Plot Lorenz Curve ROC Curves (actual data and

predicted values)

Histogram Line Plot Scatter Plot Lorenz Curve ROC Curves (actual data and

predicted values)

K-Means K-Means

Statistical ModelingStatistical Modeling

Decision Trees Decision Trees

Predictive Models Cluster AnalysisData Visualization

Classification

Machine LearningMachine Learning

SimulationSimulation

Monte Carlo Monte Carlo

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www.revolutionanalytics.com  Twitter: @RevolutionR

The leading commercial provider of software and support for the popular open source R statistics language.

Thank you