Risk Analysis in the Financial Services Industry
 

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

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

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Risk Analysis in the Financial Services Industry Presentation Transcript

  • 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 2
  • 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 3 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
  • 4. Revolution Confidential Incredible graphics, visualization and flexible statistical analytics capabilities 4 4500+ packages
  • 5. Revolution Confidential 5 has some constraints for enterprise use
  • 6. Revolution Confidential 6 Innovate for breakthroughs && Scale & power your analytics Deploy widely with confidence
  • 7. Revolution Confidential 7 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
  • 8. Revolution Confidential DistributedR and ScaleR processing handles big data and / or big analytics. 8
  • 9. Revolution Confidential 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 9 R / Statistical Modeling Expert DeployR Data AnalysisData Analysis Business IntelligenceBusiness Intelligence Mobile Web AppsMobile Web Apps Cloud / SaaSCloud / SaaS Deployment Expert
  • 10. Revolution Confidential On-Demand Analytics with DeployR 10 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) 11 • 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
  • 12. Revolution Confidential “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 12 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
  • 13. Revolution Confidential F100 Investment Co. Outlier & Error Detection 13 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
  • 14. Revolution Confidential Quantitative Research @ Global Investment Co. 14 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.
  • 15. Revolution Confidential Innovates to Outperform 15 “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
  • 16. Revolution Confidential 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 16
  • 17. Revolution ConfidentialHigh Performance R & Big Data Analytics Parallel External Memory Algorithms 17  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 18  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 19 www.revolutionanalytics.com  Twitter: @RevolutionR The leading commercial provider of software and support for the popular  open source R statistics language. Thank you