Find out how R and Revolution Analytics are helping Financial Services companies manage credit, market and operational risk.

1. Revolution Confidential
R in the Financial Services
Industry
June 6, 2013
Karl-Kuno Kunze
Neil Miller
Andrie De Vries
Breakfast Briefing

2. Revolution Confidential
R 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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3. Revolution Confidential
Revolution Analytics
Corporate Overview & Quick Facts
Founded 2007
Office Locations Palo Alto (HQ),
Seattle
(Engineering)
Singapore
London
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 Data
Predictive 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 flexible
statistical 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
ScaleR
Distributed High Performance Architecture +
High Performance Big Data Analytics
packages
RevoR
Performance Enhanced Open Source R + Open Source R
packages
g p
ConnectR
High Speed Connectors
PlatformR
Distributed Compute Contexts
DevelopR
Integrated Development
Environment
DeployR
Web Services
Revolution R Enterprise
High Performance, Multi-Platform Enterprise Analytics
Platform

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DistributedR and ScaleR processing handles big
data and / or big analytics.
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Integration Layer:
DeployR makes R accessible
 Seamless
Bring the power of R to any web enabled application
 Simple
Leverage common APIs including JS, Java, .NET
 Scalable
Robustly scale user and compute workloads
 Secure
Manage 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
Deployment
Expert

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On-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

11. Revolution Confidential
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 fit
SAS (current
tool)
16-core Sun Server Proc GENMOD 5 hours
rmr / map-
reduce
10-node (8 cores /
node) Hadoop
cluster
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)
Revolution
ScaleR
5-node (4 cores /
node) LSF cluster
90 minutes to load full data set 5.7
minutes

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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 minutes
100,000 years of simulations
Pricing 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

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F100 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 variables
Weekly 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

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Quantitative 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.

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Innovates 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 lift
Strategy simulation & portfolio
optimization
Days to overnight

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Other 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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17. 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 AnalyticsData Prep, Distillation & Descriptive Analytics
 Subsample (observations &
variables)
 Random Sampling
 Subsample (observations &
variables)
 Random Sampling
R Data Step Statistical Tests
Sampling
Descriptive Statistics

18. 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

19. Revolution Confidential
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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.
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