Stress Test Data Virtualization by Aite Group

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Stress-Test Data Virtualization: Better

Insights, Lower Costs

Prepared for:

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LIST OF FIGURES

FIGURE 1: A SAMPLE OF DATA SETS CALLED UPON DURING A STRESS TEST ... 6

FIGURE 2: BANKS' AMBIVALENCE ABOUT SUCCESS WITH DATA MANAGEMENT ... 8

FIGURE 3: BANKS' AMBITIONS FOR THEIR STRESS TESTS ... 9

FIGURE 4: BANKS SUPPORT STRESS TESTS WITH LIMITED AUTOMATION ... 10

FIGURE 5: ADVERSE REGULATORY FINDINGS A REAL RISK FOR BANKS ... 11

FIGURE 6: ASSESSING THE BENEFITS OF STRESS-TEST DATA MANAGEMENT SYSTEMS ... 14

LIST OF TABLES

TABLE A: QUANTIFYING THE IMPACT OF DATA VIRTUALIZATION ON PRODUCTIVITY ... 15

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EXECUTIVE SUMMARY

Stress-Test Data Virtualization: Better Insights, Lower Costs, commissioned by Delphix and produced by Aite Group, examines the data management challenges faced by banks that complete stress tests and identifies the benefits banks can achieve when these hurdles are overcome using data virtualization.

Key takeaways from the study include the following:

 The capital adequacy tests, often referred to as stress tests, that banks are now required to complete as a result of the global financial crisis of 2008 are significantly IT-intensive, requiring data from up to 100 systems and data sets across a bank's various lines of business, risk management functions, and subsidiaries.

 Banks have lofty ambitions for their stress-test initiatives. A survey completed by Aite Group found the majority of stress testing banks seek benefits from these activities that go beyond mere compliance and include insights about the bank's risk profile.

 Despite banks' ambitions for their stress-test capabilities, these initiatives tend to be poorly supported by automation. Aite Group's survey of stress testing banks

indicates that the vast majority of banks have stress-test capabilities that are fully or primarily manual.

 Poor automation extends to data management and data warehousing, a critical stress test-related capability with which just 45% of banks are satisfied and in which only 50% of banks have the goal of being stellar.

 When Aite Group compared data virtualization capabilities to banks' stress test-related pain points, Aite Group identified direct benefits—those benefits that are most easily achieved and readily quantified—that include reduced hardware costs and improved productivity as a result of better trust in data and avoiding data-related catastrophes.

 By using data compression and various storage optimization capabilities, data virtualization systems can significantly reduce the amount of storage required to complete a stress test.

 The indirect benefits of data virtualization for stress-test teams that Aite Group identified include faster point-in-time data set construction, the avoidance of hardware provisioning delays, and shorter stress-test project cycle times.

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INTRODUCTION

Among the lasting impacts of the global financial crisis of 2008 are costly and demanding new regulations that have profoundly changed the relationships between banks, their regulators, and their governments. Chief among a government's goals is to avoid being the financier of last resort to its banking sectors the next time a speculative bubble pops or an economic growth cycle runs out of steam. With this goal as a mandate, regulators now continually evaluate banks as potential borrowers using capital adequacy stress testing, a process in which banks estimate their capital levels under a variety of economic scenarios, some of which are bank-specific, and many of which are quite severe in their assumptions.

Suddenly in the unfamiliar and uncomfortable position of credit applicant to their regulators, banks struggle with not just the complexity of stress tests but also the challenge of providing their compliance departments with timely delivery of the right data for these vast and complex analyses. Stress tests involve up to five macroeconomic scenarios, each supported—ideally—by separate test, development, and run environments, which result in large data sets that must be stored. Stress testing data management requirements force banks to either make significant investments in hardware or compromise on such requirements, which can reduce the pace and quality of a bank's stress tests.

It is in this context that Aite Group has added to its stress-test knowledge base an examination of the potential benefits of data virtualization, a technology used by software development teams but readily applied to stress testing. This report can be used by mid-level, senior, and board-level managers at banks to identify and quantify the potential benefits of supporting stress testing with data virtualization.

M E T H O D O LO GY

This white paper is based on Aite Group's growing body of research on stress testing, which covers what is required of banks that stress test, the challenges they face when performing stress tests, and the vendors providing capabilities that automate stress tests or portions of these complex projects. Aite Group's knowledge is the result of a comprehensive request for information (RFI) completed by 10 global providers of core stress-test automation tools during Q2 2013, a survey of 18 compliance professionals involved in their banks' stress testing activities, and the firm's annual global survey of senior IT executives at banks. This research is further bolstered by the knowledge of the author, whose career spans 13 years in commercial banking and eight years in software analysis, most of which were spent considering analytics-related deployments and quantifying the benefits of technology investments.

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THE REGULATORY DATA BEAST

As banks become accustomed to the post-crisis regulatory world in which they must continually prove the adequacy of their capital and risk management to regulators, one of the biggest challenges they face is data management. When performing a stress test, banks aggregate, process, and analyze data from up to 100 applications and data sets from across banks' various lines of business, risk management departments, and subsidiaries. Figure 1 provides a sample of the types of data sets called upon during a stress test, which typically occur in numerous

instances across a bank's subsidiaries and lines of business.

Figure 1: A Sample of Data Sets Called Upon During a Stress Test

Source: Aite Group

Once a stress-test cycle is underway, a proliferation of various versions of stress-test scenarios and underlying data sets results in significant data storage and data management requirements for stress-test teams and compliance departments.

T H E S N A PSH OT - I N - T I ME C H A L LE N G E

Compounding banks' stress test-related storage challenges is the difficulty in performing snapshot-in-time analyses. Knowing that economic crises and the unfortunate conclusions of speculative bubbles are rarely synchronized with accounting cycles, regulators now demand that banks be able to perform ad hoc stress tests as of a given date. Although much attention and capital at financial institutions has been dedicated to achieving the fabled 360-degree view of a customer's or individual's risk, the snapshot-in-time view of a bank's entire risk profile can be equally difficult and costly.

• Core banking systems • Loan origination systems • Underwriting support systems

• Risk analysis and underwriting documents • Ratings bureaus

• Business intelligence capabilities • Treasury department systems of record • Manual surveys of individual assets and

obligors completed by underwriting and risk personnel • Loan balances • Maturity dates • SIC codes • Geographic data • Repayment histories • Internal risk ratings

• Credit sensitivities of individual assets and obligors

• Collateral data

Data points obtained

Data sources accessed

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With regard to stress testing, a snapshot-in-time view is like a pop quiz on enterprise-wide risk, which is difficult enough. The fact that such a quiz can occur on the scale of a Bank of America or a large regional bank makes it vastly difficult and creates significant IT challenges. Some of the challenges in performing a snapshot-in-time analysis include:

 Multiple environments: As is common with processes that create content for

influential outsiders such as regulators, stress tests often generate a large number of iterative drafts, test runs, and backup copies. Further causing banks to create

multiple versions of their stress tests are the various economic scenarios that must be run that—for the largest banks—consist of a regulatory base case, regulatory down case, regulatory worst case, global shock case, bank-specific base case, and bank-specific worst case. Aite Group finds that, due to the importance of stress tests, the environments that banks seek to create in support of stress tests can mirror those for application development and include separate environments for developing, testing, and running stress tests for each of the scenarios.

 Volatility: Banks spend weeks and sometimes months completing a single stress test with its multiple scenarios. While in flight, the vast number of data points

comprising a stress test are all extremely variable. For example, regulatory requirements change, their interpretations can be modified, risk ratings are adjusted, loan balances fluctuate, and risk ratings change. Such fluidity of data means that ensuring that all data records are accurately and uniformly updated across all scenarios and environments is a task not readily accomplished in the absence of automation. Data volatility can also impact performance; the more variable data a stress test must accommodate, the more slowly it will perform scenario analyses and publish reports.

 Volume: Stress tests, and the data sets they comprise, are extremely large. These examinations, especially if they are performed with loan-level granularity, require information on a large number of portfolios, loans, and loan-specific data points such as interest rates, risk ratings, and economic sensitivities. Enterprise-wide in nature, a bank's stress-test capabilities must encompass all commercial loans, derivatives entered into on behalf of clients, retail loans, car loans, mortgages, lines of credit, and every position entered into by treasury desks. Adding to the

complexity and volume are the required projection scenarios, which comprise 31 domestic and international economic metrics over a nine-quarter period.  Storage: Stress tests, their results, and their underlying data sets, due to their

vastness, require large amounts of storage. Although banks often minimize the hardware-related costs of applications such as customer relationship management (CRM) by using existing on-premise server space, or turning to software as a service (SaaS) or cloud-based storage, these cost-avoiding tactics are not available for stress tests. Comprising an analysis of a bank's capital under a variety of projections, some of which might be unfavorable, a stress test contains data and analyses that

compliance departments must store with significant levels of security and governance. Accustomed to achieving such levels of security with on-premise deployments, banks tend to make significant hardware-related investments for their

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stress tests, including the multiple scenarios, underlying data sets, and snapshot-in time versions.

DATA F I R E D R IL LS

As a result of the diversity of data and sources called upon during a stress test, it is common for bank compliance departments to experience a number of data-related fire drills when

performing a stress test. For example, if rules for combining credit-ratings databases from different subsidiaries are not crafted properly, scores can default to zero or "not applicable." Many banks remedy such problems by having administrative or underwriting staff rebuild such data sets from scratch, a lengthy, costly, and error-prone process. The more manual and

spreadsheet-based a bank's stress-test capabilities are, the more likely such data disruptions and productivity losses will be.

BA N K S' STAT E O F BAT T LE R EA D IN ES S

Aite Group finds that with regard to data in general and stress testing in particular, banks may not be fully prepared to battle the regulatory data beast. In its most recent global survey of senior IT executives at large banks across the world, Aite Group found banks to be relatively dissatisfied with their capabilities related to data management and data warehousing (Figure 2).

Figure 2: Banks' Ambivalence About Success With Data Management

Source: Aite Group's global survey of banks with more than US$10 billion in assets, Q1 2014

Aite Group observes two concerns in Figure 2 . First, the dissatisfaction rates that banks have for data management and business intelligence/performance management were among the highest of 53 technologies examined by Aite Group. Second, banks appear to lack ambition in these spending areas; only half or less have the goal of being stellar at both critical capabilities. If banks have not only limited capabilities in these areas that are so key to stress testing but also limited goals and spending, it's unlikely they'll be able to overcome the data management and storage challenges that come with stress testing.

When Aite Group asked about banks' stress testing capabilities in particular, it found banks to be of two minds: although banks are relatively ambitious about their stress testing capabilities,

Yes No Do not have

Be good

enough Be stellar Down Flat Up Data warehousing/data management 45% 49% 5% 50% 50% 7% 23% 70% Business intelligence/performance management 48% 47% 5% 51% 49% 7% 36% 57% Big-data analysis tools 30% 52% 18% 52% 48% 8% 16% 77%

Average 41% 49% 10% 51% 49% 7% 25% 68%

(% of total respondents) (% of respondents that have the technology)

(% of respondents that have the technology)

Q. Please help us understand your institution's IT initiatives in data management and analysis. (Average n=77)

Q. Is your firm satisfied with this capability?

Q. Firm's goal with

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these systems—despite their importance to one of the most demanding regulatory challenges of the day—benefit from limited technological support. When surveyed about their goals for their stress tests, the majority of respondents indicate a desire to gain insights from these complex analytical and reporting projects. In Aite Group's recent survey of stress testing banks, only a minority of banks seek merely to comply with stress-test regulation; the majority indicate that they either stress test to achieve both compliance and insights or that insights were so

important when performing stress tests that compliance is only a byproduct (Figure 3). Unfortunately, though banks appear to be relatively ambitious about their stress-test

capabilities, Aite Group finds that these initiatives tend to be poorly supported with automation (Figure 4).

Figure 3: Banks' Ambitions for Their Stress Tests

Source: Aite Group's survey of 18 compliance professionals, Q2 2013

We want to comply and achieve insights, 12 We just want to comply, 3 We stress test to gain insights; compliance is a byproduct, 3

Q. In managing its stress-test capabilities, which statement best describes your bank's goals?

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Figure 4: Banks Support Stress Tests With Limited Automation

M E D IO C R I T Y I S N OT A N O PT IO N

Aite Group sees significant risk in banks' lack of support for their stress-test capabilities. First, there is regulatory risk. The more poorly automated stress tests are, the more likely they are to contain errors that invoke adverse findings—and require costly remedies—by regulators. Indeed, surveying by Aite Group indicates a meaningful portion of banks have either received adverse feedback from regulators, fear such an outcome, or have no idea how satisfactory their stress-test capabilities will be in the eyes of regulators (Figure 5).

Partially manual, partially automated, 12 Fully or primarily manual, 5 Fully or primarily automated, 1

Q. Which of the following statements best describes the level of stress-test automation at your bank?

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Figure 5: Adverse Regulatory Findings a Real Risk for Banks

Errors and adverse findings when stress testing are no small issue. When, in April 2014, a large Tier-1 CCAR Bank identified a longstanding error in its stress-test submissions and brought it to the attention of the Federal Reserve, the result was suspension of both a planned dividend increase and stock repurchase plan, causing the bank's stock to fall by 6.2%, a reduction to market capitalization of approximately US$10 billion.

Productivity is also an issue. Aite Group finds that although banks don't always indicate costly penalties or citations as costs of regulations, this is because they achieve what Aite Group calls "compliance at any cost." Seeking to avoid the fines and negative press that result with

regulatory findings but lacking the centralized databases and automation required for complex reporting, many banks achieve compliance by building small armies of mid- and low-level analysts and report builders. Aite Group has anecdotal evidence that, among larger banks, these departments can number in the several hundred, bloating banks' payroll costs by millions of dollars.

ST R E S S - T E ST DATA VI RT UA L IZAT I O N

In an effort to identify solutions to banks' stress test data-management challenges, Aite Group has examined one data management capability: Delphix Virtual Data Platform (Delphix). Though originally designed to support software development, this solution has been configured for use by the stress-test teams of several large banks. In examining Delphix and talking with banks that have struggled to create cost-effective stress-test capabilities, Aite Group identifies the features that can improve a bank's management of stress test-related data.

Comments from regulators have been favorable, 6

No comments, but we're confident

our tests will comply, 4 Comments from regulators have been unfavorable, 2 No comments,

and we're not confident our tests

will comply, 1 Don't know, 5

Q. Which statement best describes regulators' comments about your bank's stress-test capabilities?

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D A T A V I R T U A L I Z A T I O N

Delphix uses four primary features to reduce the amount of storage a bank requires to support its stress-test activities:

 Block mapping: This feature takes advantage of the fact that although stress tests require multiple versions of large amounts of information, a significant portion of this data does not change from one version, scenario, or copy to the next. For example, while banks tend to set up individual environments and data sets to test different assumptions about how their assets will respond to a particular economic scenario, the vast majority of the data in these assets does not vary from one

environment to the other. Within such environments, all of the data about the loans, their balances, their risk ratings, and economic sensitivities constitute the majority of a stress-test data environment and will remain unchanged across all the

environments. The portion of data that changes across the environments is a relatively small portion of the data set and consists largely of the downstream impacts of analysts' assumptions about how bank assets will respond to an economic scenario.

Delphix conserves storage space requirements by first identifying the blocks of unique data within the stress-test environment that will change from one environment to the next. When new versions of the stress-test environment are required, they consist of only such unique blocks of data, which are then combined with the remainder of the data set that does not change, on only an as-needed basis. By storing only once the portion of a stress-test data set that is static and constitutes a significant portion of the overall data, Delphix significantly reduces the amount of storage required to support a stress-test environment.

 Efficient updating: Once Delphix identifies and stores the static portion of a stress-test data set and changes to data points are required, Delphix maximizes speed by accessing and changing only the data blocks that are variable.

 Compression: Delphix further reduces the amount of storage required for a stress test by identifying gaps in data and blank fields to avoid unnecessary space usage, then applying industry-standard compression techniques to further reduce the space requirement.

 Bookmarking: Delphix enables compliance staff to bookmark and archive iterations

of both a stress test and the underlying point-in-time data sets for safekeeping, much like using "save as" to create a draft version of a Word document. Compression and block mapping enable such versions to be retained without a significant addition to the required memory footprint.

C O N T I N U O U S P O I N T - I N - T I M E C R E A T I O N

An important feature of data virtualization capabilities is their ability to be continuously on and monitoring all of the applications and data sets relied upon for the performance of a stress test. As a result, Delphix can accommodate the demanding ad hoc, pop-quiz nature of Dodd-Frank stress tests by creating a snapshot of stress test-related systems as of any point in time. For

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example, if a bank using Delphix to manage its stress test-related data sets and applications is suddenly informed that it must perform a stress test as of a randomly chosen date three weeks prior, Delphix can create a virtual version of every application and data set as of the end of the exact business day required for a stress test.

S E L F - S E R V I C E M A N A G E M E N T

Stress-test data virtualization systems also provide administrative capabilities that enable self-service management and are designed for use by compliance or risk management staff who lack technical training. These capabilities include defining users and groups of users, specifying what data sets—or copies thereof—each can access, and whether each can connect to new sources. Similar capabilities exist for governing the archiving of financial records as of required dates and retaining them over the time periods required for regulatory compliance. Self-service

management capabilities can also be used to schedule data refreshes and distribution of data sets to stress-test team participants.

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BENEFITS OF STRESS-TEST DATA MANAGEM ENT

SYSTEMS

In examining the capabilities of stress-test data management systems and comparing them to the challenges faced by stress testing banks, Aite Group identified two primary benefits of these systems: the avoidance of hardware costs and productivity improvements caused by better data management. Identified in Figure 6, with their respective magnitudes, achievability, and ease of quantification, these benefits warrant significant consideration by banks seeking to improve the cost effectiveness of their stress testing procedures and capabilities. Identified as particularly achievable and quantifiable was the benefit of reduced infrastructure costs.

Figure 6: Assessing the Benefits of Stress-Test Data Management Systems

I M PROVE D PR O D UC T I V IT Y

Aite Group anticipates that banks using stress-test data virtualization can achieve productivity increases as a result of fewer data catastrophes, better trust in data, and more rapid

construction of as-of data environments.

Low Low High High Achievability

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f q

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fi

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Avoidance of provisioning delays Shorter project cycle times Less costly point-in-time construction Avoidance of data reconstruction projects Better trust in data Improved productivity Reduced infrastructure costs

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F E W E R D A T A C A T A S T R O P H E S

When stress-test teams are equipped with stress-test data virtualization capabilities such as compression, block mapping, and bookmarking it becomes far less costly to save multiple versions of stress-test data sets. With more saved versions of the stress-test data environments, stress-test teams can recover from data catastrophes far more rapidly: Rather than perform a labor-intensive sequence of root-cause analysis and data reconstruction, teams simply roll back the environment to a pre-disruption point-in-time version or bookmark.

B E T T E R T R U S T I N D A T A

Although many banks perform stress tests without virtualization capabilities, and data sets can be rebuilt or repaired when data catastrophes arise, lingering doubts about the data can be costly. When unsure about the quality that underlies a stress test, managers—both in

compliance roles and in the lines of business impacted by the results—typically respond with multiple layers of verification. In such environments, underwriters often confirm that the source data is accurate, loan officers perform a sanity check on the performance of their loans under projected conditions, and managers who supervise teams of lenders examine the underlying data used to stress the portfolios for which they are responsible. Lastly, senior managers

responsible for the accuracy of submissions to regulators also perform double checks in an effort to not wind up in an orange jumpsuit. Conversely, when stress-test teams benefit from fewer data catastrophes and data reconstruction projects, a higher level of trust in data is the result— for both stress-test team members and internal consumers of stress-test results who take responsibility for their banks' submissions to regulators.

Q U A N T I F Y I N G T H E P R O D U C T I V I T Y B E N E F I T

Aite Group estimates that seven roles can be impacted when data virtualization both reduces the number of data catastrophes and increases the level of trust in data. The resulting productivity improvement has been estimated by Aite Group in Table A.

Table A: Quantifying the Impact of Data Virtualization on Productivity

Role Benefit Hours of

work eliminated per cycle Average fully loaded cost per hour (US$) Head-count Gross annual savings (US$)

Underwriter Avoided data reconstruction; eliminated verification of risk data at the loan level

40 $51.92 60 $249,231

Loan officer Eliminated verification of risk data at the portfolio level

20 $97.36 10 $38,942

Lending group manager

Eliminated verification of risk data at the portfolio level

20 $129.81 3 $15,577

Mid-level compliance

Avoided data reconstruction, eliminated verification of risk data at various levels, reduced

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Role Benefit Hours of work eliminated per cycle Average fully loaded cost per hour (US$) Head-count Gross annual savings (US$)

staffer manual reporting

Senior-level compliance staffer

Avoided verification of risk data at the portfolio level and manual reports

60 $84.38 3 $30,375

Chief compliance officer

Reduced time spent verifying and auditing stress-test inputs and results

30 $129.81 1 $7,788

Chief risk officer

Reduced time verifying and auditing stress-test inputs and results

30 $129.81 1 $7,788

Total $630,087

M O R E R A P I D A S - O F D A T E D A T A R E C O N S T R U C T I O N

A less quantifiable benefit of a stress-test data virtualization system identified by Aite Group is the ability to reduce the time and labor required to complete a profile of the bank's assets, exposures, and risk-related applications as of the date randomly chosen by the regulator for a stress test. For the largest banks, regulators perform bank stress tests in a fashion much like a pop quiz. Twice a year, banks are informed that they must not only perform a stress test, but also do so as of a randomly chosen date during the preceding fiscal quarter. Building data sets that accurately reflect the state of all risk-related systems and databases at the end of business on the stress-test as-of date is extremely labor-intensive, incorporating multiple versions across the bank of assets identified in Figure 1. Up to 100 systems and data sets can be involved, only some of which will have been properly backed up on the as-of date; some data sets will be hard to identify and locate. Further complicating the challenge is the breadth of data that must be obtained across a bank, including every subsidiary, line of business, and even the treasury department, where exposures are likely to include complex assets such as hedges, which are difficult to model in an economic projection.

Aite Group estimates that by providing continuous point-in-time recreation for the sources and data sets in Figure 1, data virtualization systems enable banks to significantly reduce the amount of labor required to create as-of data environments. Though difficult to quantify, Aite Group estimates that among the hundreds of data sets for which accurate as-of-date versions are required, at least 10% will be unavailable or corrupt, requiring either repair or rebuild by stress-test teams, data owners, or staff in lending lines of business. Table A is based on a small to mid-sized bank; Tier-1 and Tier-2 banks that adopt stress test data virtualization will likely experience far higher savings.

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A V O I D I N G P R O V I S I O N I N G D E L A Y S

By enabling stress-test teams to independently fulfill their data requirements, data virtualization capabilities can eliminate provisioning delays that disrupt analytical processes and lengthen project cycle times. Stress testing's significant data requirements, including the provisioning of data required for test, development, and run environments for multiple scenarios leave stress-test teams highly dependent on other departments, such as procurement and IT. Compared to compliance departments, such organizations are typically far more process-oriented and less concerned with regulatory mandates. Additionally, the acquisition and usage of hardware for a task as complex as stress testing is about far more than procurement, as devices need to be configured, partitioned, secured, and equipped with varying levels of permission for different roles within a stress-test team. As a result, stress-test teams often wait weeks or months for the substantial storage assets they require. Although such delays may be tolerable for an in-house software development team in a multi-year project, they are extremely disruptive for a stress-test team with only 13 weeks to perform vastly complex projections of their banks' capital levels. With data virtualization that can reduce storage requirements by an order of magnitude,

compliance departments can accomplish far more with their existing storage space and make fewer hardware provisioning requests in a given stress-test season.

Although not readily quantified, avoiding provisioning delays and interruptions is considered significant by Aite Group. Stress tests are vastly complex projects requiring data, analysis, and collaboration among a large number of bank employees and departments. Stress tests are also a highly analytical and intellectual project incorporating a thorough understanding of a bank's risk profile and multiple projection scenarios. The less such a project is delayed or interrupted by waiting periods for additional provisioning, the more continuous, accurate, and informed the stress-test team’s data aggregation and analyses will be.

S H O R T E R S T R E S S - T E S T P R O J E C T C Y C L E T I M E S

Aite Group expects shorter project cycle times to be the net result of fewer data reconstruction projects, better trust in data, and avoided provisioning delays. Here, two outcomes are

important to Aite Group. First, with shorter project cycle times, banks are better able to meet regulators' demanding stress-test timetables and avoid the compliance-at-any-cost practice of dedicating more staff to a stress test in an effort to accelerate it. Better analysis is an even more important benefit. With the ability to complete stress-test analyses more rapidly, and with fewer interruptions or delays, banks will have more time to use stress testing to achieve risk-related analytical insights rather than just complete the process as a regulatory mandate—an important goal for banks, as indicated in Figure 3.

R E D U C E D STO R AG E CO ST S

A primary benefit of data virtualization is a substantial reduction in the amount of storage required. The volume of data required to support a stress-test environment is significant and driven by factors that include the number and size of risk-related assets such as enterprise applications, their underlying data sets, macroeconomic scenarios, and regulatory requirements that final versions of stress tests be maintained after their completion. Depicted in Table B, these demands result in potential storage requirements that are cost-prohibitive for most banks. As a

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workaround and cost-avoidance tactic, banks typically build their development, test, and run environments to support all of their projection scenarios. Although this workaround can reduce costs, it also has several drawbacks. First, having one set of environments for all scenarios makes it more difficult for stress-test teams to maintain and govern separate data sets and scenarios. Performance is also an issue; the more scenarios a given development, test, or run environment supports, the more slowly applications in the environment will complete analyses and build reports.

Table B: Stress Test-Related Storage Costs

Optimal Cost optimized

Number of stress test-related systems and data sets 100 100 Average size of a system or data set in terabytes (TB) 1.00 1.00

Number of scenarios modeled 6 6

Number of environments (development, test, run, archive) 4 2

Total system instances required in memory 2400 1200

Average cost per TB (in US$) $635 $635

Total cost (in US$) $1,524,000 $762,000

Aite Group finds that data virtualization can significantly reduce the cost of storage required for stress-tests with related data virtualization capabilities. In fact, Aite Group estimates that by applying techniques such as compression, block mapping, and efficient updating, banks can reduce stress test-related storage requirements by up to 90%. Accomplishing this can result in a significant reduction to hardware costs for stress testing banks (Table C).

Table C: Stress Test-Related Storage Costs With Virtualization Techniques

Optimal Optimal with

virtualization Cost optimized Cost optimized with virtualization

Number of stress test-related systems and data sets

100 100 100 100

Average size of a system or data set in TB 1.00 0.10 1.00 0.10 Number of scenarios modeled 6 6 6 6 Number of environments (development, test, run, archive)

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Optimal Optimal with virtualization Cost optimized Cost optimized with virtualization

Total system instances required in memory

2400 240 1200 120

Average cost per TB (in US$) $635 $635 $635 $635

Total cost (in US$) $1,524,000 $152,400 $762,000 $76,200

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CONCLUSION

Given the availability of virtualization technology that can dramatically increase data

management capabilities, banks should consider the addition of such capabilities to their stress-test technology roadmap. By significantly reducing the amount of storage required to support a stress test, virtualization can also reduce the capital expenditures required for these analyses. By enabling reliable, cost-effective bookmarking and archiving of multiple versions of stress-test data sets, virtualization can also improve stress-test team productivity by reducing the number of data catastrophes while also improving participants' trust in both their analyses and the data on which they rely. Due to their continuous ability to perform a point-in-time recreation of a data set or application, data virtualization capabilities can also significantly reduce the volume of manual labor required to construct the highly detailed and data-intensive analysis of bank's risk profile as of the date required for a stress test. By enabling stress-test teams to more

independently fulfill their data requirements, virtualization reduces the number and duration of provisioning delays that can interrupt the complex collaboration and analysis required for a successful stress test.

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ABOUT AITE GROUP

Aite Group is an independent research and advisory firm focused on business, technology, and regulatory issues and their impact on the financial services industry. With expertise in banking, payments, securities & investments, and insurance, Aite Group's analysts deliver comprehensive, actionable advice to key market participants in financial services. Headquartered in Boston with a presence in Chicago, New York, San Francisco, London, and Milan, Aite Group works with its clients as a partner, advisor, and catalyst, challenging their basic assumptions and ensuring they remain at the forefront of industry trends.

AUT H O R I N FO R MAT IO N

David O'Connell

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[email protected]

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