An Oracle White Paper January Time to Re-Model? Oracle Financial Services Analytical Applications R Modeling Framework

Transcription

1 An Oracle White Paper January 2014 Time to Re-Model? Oracle Financial Services Analytical Applications R Modeling Framework

2 Disclaimer The following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle s products remains at the sole discretion of Oracle.

3 Executive Overview... 1 Introduction... 2 Modeling Dilemmas... 4 Comprehensive, Metadata-Driven Solution... 4 Oracle Raises the Power of R... 5 Connecting the Dots... 6 A New Approach to Model Creation, Validation, Governance... 7 Spanning the Enterprise... 8 Conclusion Time to Re-Model?

4 Executive Overview Financial services organizations including banks, capital markets firms, and insurers are rethinking modeling across their enterprises. This renewed focus is fueled, in part, by new regulatory requirements. As important, however, financial institutions are increasingly incorporating analytical insights into their operational decision processes. In addition, statistical modeling is taking on a wider role within the enterprise as institutions weave prediction, optimization, and forecasting models into their enterprise analytics fabric. New challenges come with increased adoption. As model outputs are incorporated into regulatory and other strategic business intelligence processes, enterprise model management, much like enterprise data management, must become a priority. Further, even as modeling becomes more prevalent in the enterprise and as models are increasingly deployed on centrally owned IT department platforms, a chasm remains between the modeling and IT worlds. To meet these and other challenges, Oracle has introduced a new meta-data driven, enterprise-modeling framework as a part of Oracle Financial Services Analytical Applications Infrastructure. This new framework brings the power and flexibility of open source R, delivered via Oracle s open-standards-compliant, in-database R engine. To this core R modeling capability, the framework adds enterprise model management and governance capabilities that financial service organizations require. With this new framework, the Oracle Financial Services Analytical Applications Infrastructure toolset provides a unified environment for financial services analytics data management and model lifecycle management. Oracle Financial Services Analytical Applications R Modeling Framework enables financial organizations to weave model management into broader risk management, customer insight, and financial crime detection application initiatives; increase transparency; and improve model governance and control all while reducing complexity and costs. 1

5 Introduction In addition to their long-standing role in risk management, statistical models are increasingly the foundation of customer insight and marketing, financial crime and compliance, and enterprise financial performance management analytical applications. As a result, organizations are spending more time and resources validating models, improving data quality, verifying results, and managing and governing the use of models across the enterprise. As modeling finds wider use within financial institutions, open source modeling platforms, such as the R statistical platform, have become enormously popular for enterprise risk, financial fraud, and other financial services analytical use cases. There is good reason for this development. Open source software not only drives down costs, but over the long term, the open community typically drives innovation faster than any single organization. R is widely taught in universities and has become a go-to language for analysts worldwide. According to Rexer Analytics 2103 Data Miner Survey, R is the most popular data mining tool among respondents, with 70 percent reporting that they use it. It is also the data tool used most often by the survey s more than 1,259 respondents, with 24 percent citing it as their primary tool. R adoption continues to grow rapidly and, since it is open-source, the language is evolving at an exponential pace, with users worldwide fueling innovation. The language is purpose-built for statistical model building and data analysis tasks, and because it is a an open source platform, statisticians and developers from the community are constantly expanding the functionality of the base platform by adding new functional packages saving time and costs and accelerating innovation within enterprises. An enterprise-level analytics platform must be more than just a statistical modeling package. The R platform by itself does not provide the comprehensive data management and governance capabilities that banks require to meet business and regulatory requirements. It also does not provide model management and deployment capabilities and does not enable 2

6 organizations to easily integrate model outputs into applications. An enterprise analytics platform should leverage the power of R and extend it. Oracle is providing an answer to this challenge with the Oracle Financial Services Analytical Applications R Modeling Framework. It delivers a complete solution that combines:. 3

7 Modeling Dilemmas Even though the models that statisticians develop have to be deployed and managed on bank IT systems, modelers and IT often do not share a common toolset or even seem to speak the same language. This slows down processes. It is not uncommon, for example, for it to take several weeks for a model to be deployed after it is developed. IT departments have developed mature principles and tools for systems lifecycle management and much of the methodology could be adapted for model lifecycle management. However, because of the disconnect between modelers and IT, the processes and methodologies remain separate. From a data management perspective, modeling platforms often work on copies of enterprise data. So, while a bank may have put in place sophisticated data governance policies around data in the enterprise warehouse, data used for models are often outside the purview of these governance systems. The oftrepeated phrase that the analytics problem is a data problem underscores the need to closely tie analytics and data management. Yet, while banks have poured resources into enterprise level data management and governance programs, enterprise-level model management does not seem to have attracted quite the same level of attention. This is likely to be short sighted. Regulatory requirements have shaped a financial institution s data management approach, and there is no reason to assume that regulators demands for model management will be any different. Further, model outputs (such as scores) are integral to business decision making, but model outputs in and of themselves are not readily usable in business decisions. They need to be interpreted and delivered via business applications. For example, a credit risk stochastic economic capital model might compute the required capital value, but the business may want to see that value allocated to individual exposures via a set of deterministic business rules (i.e. an application). In effect, models should not be executed in isolation. Instead, they should be exposed as services that may be stitched together with other business process logic to form a complete analytical application. Finally, data volumes continue to grow rapidly while the turnaround time for analytical outputs continues to shrink adding another layer of complexity. Moving model execution to where the data resides has become a necessity today. This in-warehouse analytics approach, of course, necessitates a tight integration between the warehouse software and the modeling platform. Oracle Financial Services Analytics Application Infrastructure R Modeling Platform is designed to help address these issues, bringing together data management, statistical modeling, and business intelligence application development capabilities in a single analytics platform. Comprehensive, Metadata-Driven Solution Oracle Financial Services Analytical Applications Infrastructure is a unified platform in that complete analytical applications from data management to model development and deployment to incorporating model outputs into business usable applications may be built, deployed, and managed using the platform. It is architected to help bridge the IT-statistical modeler divide and integrate with IT security and deployment policies. 4

8 Enforcing IT governance policies uniformly across data, model, and application objects becomes possible when all objects are represented in a unified metadata management system. The metadatabased platform manages models like other application objects. For example, the same IT governance, security, auditability procedures that govern data and business rules objects may be applied to models. Models, like business processes are discoverable and callable service-orientated objects that are registered in the platform s metadata object repository. Building a complete analytical application via the platform is as straight-forward as orchestrating the sequence of execution of different metadata registered objects. Models access data, but modelers should not be expected to be SQL or NoSQL technology experts. Metadata-driven modeling means models work on variables that are also metadata objects. Behind the scenes, the system takes care of mapping these variables to their data source, thus freeing the statistician from having to deal with the complexities of data management technologies. Oracle Raises the Power of R R is the core modeling and data analysis platform that powers Oracle Financial Services Analytical Applications R Modeling Framework. The open source distribution of R has some limitations however, and, in response, Oracle has developed a compatible alternate distribution called Oracle R Enterprise which is a component of Oracle Advanced Analytics, an option of Oracle Database Enterprise Edition. Analyzing huge data sets presents a challenging opportunity for financial services organizations, which are working to balance the maintenance and support of existing IT infrastructure with the need to analyze rapidly growing data stores. Increasingly, statisticians and data scientists need to do advanced computations on large amounts of data. To assist these professionals, Oracle has created a broad set of options for conducting statistical and graphical analyses on data stored in Hadoop or Oracle Database, bringing enterprise-caliber capabilities to projects that require high levels of security, scalability, and performance, such as financial modeling. In many cases, processing this data requires a fresh approach because traditional techniques fail when applied to massive data sets. To extract immediate value from big data, financial organizations require tools that efficiently access, organize, analyze, and maintain a variety of data types. Oracle R Enterprise offers an innovative solution. It integrates the popular open-source R statistical programming environment with Oracle Database, delivering in-database server R engine execution. Users can run R commands and scripts for statistical and graphical analyses on data stored in the Oracle Database and leverage its parallelism and scalability to automate data analysis. Running as an embedded component of the database, Oracle R Enterprise can run any R package and offers the advantage of high performance and scalability required when analyzing large amounts of data. Organizations also do not need to install any software on the desktop, reducing licensing costs and maintenance requirements. Since the R engine runs in the database itself, financial organizations can avoid moving large amounts of data, a practice that impairs performance and increases security risks. 5

9 Connecting the Dots Oracle Financial Services Analytical Applications R Modeling Framework, which leverages and builds on Oracle R Enterprise, presents a metadata-based approach for building analytical applications and statistical models in R. It enables financial services organizations to perform in-warehouse analytics and integrate model management into broader risk management initiatives, increase transparency, and improve model governance and control. The new modeling framework is part of the Oracle Financial Services Analytical Applications suite, industry leading applications that enable financial institutions to actively incorporate risk into decision making; achieve a consistent view of performance; promote a transparent risk management culture; deliver actionable customer, business line, and product profitability insight; and enable pervasive intelligence. Oracle Financial Services Data Foundation underpins Oracle s analytical applications, which integrate risk performance management, customer insight, and financial compliance on a single platform, data model, and application architecture. The foundation, which serves as a ready-to-deploy, industry proven, practical platform for managing analytical applications data, has three key features: Comprehensive financial services physical data model that enables organizations to deploy their analytical data warehouse in a fraction of the time typically required to develop a warehouse from a conceptual or logical data model Integration with an industry-leading analytical application infrastructure A highly optimized and tuned Oracle physical infrastructure consisting of Oracle Database running on Oracle Exadata Database Machine, ensuring that the platform takes full advantage of built-in analytical capabilities and performance-enhancing features of the underlying database ecosystem 6

10 Time to Re-Model? 7

11 Spanning the Enterprise Oracle Financial Services Analytical Applications R Modeling Framework has use cases across the enterprise from performance management to customer insight. Enterprise Performance Management (EPM) In April 2011, the U.S. Office of the Comptroller of the Currency and the Federal Reserve Board issued Supervisory Guidance on Model Risk Management. The directive outlined new guidelines for model creation and use and called for more rigorous model verification and validation, as well as more stringent governance and control. Since Oracle Financial Services Analytical Applications R Modeling Framework enables institutions to separate models from applications and because R is a statistical language, it is easier for organizations to validate, audit, and track models. The framework also enables organizations to unify important, but often disparate, factors and variables that are essential to core business processes. For example, to determine optimal mortgage rates, a financial institution needs to understand costs as well as how many customers are likely to prepay and at what point in their mortgage. Enterprise Risk Management (ERM) One of the greatest lessons of the financial downturn was that financial institutions need greater visibility into the interconnectedness of various types of risk. For example, how might a change in interest rates affect market risk, credit risk, and more? Also, institutions face expanded stress testing mandates that require them to bring various types of risk information into a common platform, which can be a complex and labor-intensive undertaking. Oracle Financial Services Analytical Applications R Modeling Framework helps organizations to address these new requirements and ensure consistency across models. It enables a unique scripting environment that allows users to define their own techniques and use their own modeling methodologies, as well as share models across the enterprise. A common data foundation also enables organizations to access and leverage many different data sets for greater transparency. Financial Crime and Compliance Fraud detection requires real-time alerts, so application performance is critical. Oracle Financial Services Analytical Applications R Modeling Framework supports these demands by eliminating the need to move data from the database to the application layer as the Oracle R Enterprise engine runs in the database environment. As important, financial crime continues to evolve rapidly, so it is important for firms to continually evaluate and update their analytical models. The Oracle framework makes it easy to regularly refresh fraud models, and allows organizations to benefit from the collective knowledge of the open-source R community. The solution also enables organizations to manage model lifecycles across a large team of statisticians for greater insight and responsiveness. AML presents a different type of modeling challenge. In the case of AML, the focus is on eliminating false positives. Since R is particularly well suited to identifying and analyzing outliers, it delivers a strong value proposition for such use cases. Customer Insight Oracle Financial Services Analytical Applications R Modeling Framework supports transactional analysis that can yield critical insight about future customer propensities for a particular product or channel and enable more targeted offers and outreach. This framework also enables development of effective attrition models that will help the bank identify customers at risk of 8

12 attrition and take corrective measures. For example, a bank might use the credit card attrition model to determine which credit card customers are at greatest risk of leaving the bank and then develop relevant retentions offers and/or product packages tailored to suit the customer s unique needs. This can then be presented via the channel for which the customer has shown the highest propensity to respond. Insurance Risk Regulation Across the globe, regulators are formulating legislation that puts a greater emphasis on insurance companies modeling their risks in a more transparent way, especially risks to which a notional capital cost can be ascribed. For example, the National Association of Insurance Commissioners (NAIC) in North America and the European Insurance and Occupational Pensions Authority (EIOPA) have proposals/laws commencing next year that require insurance companies to take a much more forward-looking approach to assessing the capital they should hold against the risks they face. Both bodies term this as the Own Risk and Solvency Assessment (ORSA), and the modeling implications are similar: Take current valuations of your liabilities; stress these liabilities; and then pull these stresses together to determine an overall risk capital figure. Then, project that into the future on some common assumptions and assess how risk capital will change over time. Insurance companies, particularly those in Europe, have been working toward these goals. Early on, they realized their underlying modeling solutions for baseline valuations were not agile enough to be extended, and for the composites, they had completely different modeling systems for their lines of business. Some companies have embarked on complex internal model solutions using large-player risk engines. These have been at a considerable expense, but appear to have yielded few agility gains in many cases. Other companies have waited for the regulation detail to develop, and it is most likely these companies that are best poised to exploit the Oracle Financial Services Analytical Applications R Modeling Framework. To perform the latter steps of establishing a risk capital forecast, the R modeling language has an arsenal of relevant predicative and statistical techniques. Without good access to the underlying data, the process is cumbersome; however, with Oracle R Enterprise, it becomes easy. When Oracle Financial Services Analytical Applications R Modeling Framework is deployed, risk capital forecast development comes together as an enterprise process with all the controls, audit functions, and governance required as part of the regulation. The solution is also scalable to meet growing modeling demands and stands to play a greater part in the underlying valuation modeling process as existing models come to the end of their practical lives. 9

13 Conclusion Models have been and will continue to be an essential tool for managing performance, risk, crime, and customer insight in the financial services sector. As model complexity and regulatory scrutiny increases, organizations are looking for new ways to effectively manage and optimize their use across the enterprise. Oracle Financial Services Analytical Applications R Modeling Framework gives financial institutions new power to perform in-warehouse analytics and integrate model management into broader risk management initiatives, increase transparency, and improve overall model governance and control. Part of the Oracle Financial Services Analytical Applications suite, the new framework: allows firms to centrally manage and control models in a single enterprise repository; enables wider model reuse and rapid integration with applications; accelerates model development; cuts risk with a safe sandbox; and streamlines compliance with stress testing requirements. Oracle s innovative solution is poised to bring new precision and governance to modeling initiatives, helping firms to gain unprecedented insight that helps to ensure compliance, reduce risk, and boost performance. 10

14 Oracle Financial Services Analytical Applications R Modeling Framework January 2014 Oracle Corporation World Headquarters 500 Oracle Parkway Redwood Shores, CA U.S.A. Worldwide Inquiries: Phone: Fax: oracle.com Copyright 2014, Oracle and/or its affiliates. All rights reserved. This document is provided for information purposes only, and the contents hereof are subject to change without notice. This document is not warranted to be error-free, nor subject to any other warranties or conditions, whether expressed orally or implied in law, including implied warranties and conditions of merchantability or fitness for a particular purpose. We specifically disclaim any liability with respect to this document, and no contractual obligations are formed either directly or indirectly by this document. This document may not be reproduced or transmitted in any form or by any means, electronic or mechanical, for any purpose, without our prior written permission. Oracle and Java are registered trademarks of Oracle and/or its affiliates. Other names may be trademarks of their respective owners. Intel and Intel Xeon are trademarks or registered trademarks of Intel Corporation. All SPARC trademarks are used under license and are trademarks or registered trademarks of SPARC International, Inc. AMD, Opteron, the AMD logo, and the AMD Opteron logo are trademarks or registered trademarks of Advanced Micro Devices. UNIX is a registered trademark of The Open Group. 0114