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Most banks are able to stand up to quantitative stress testing and even prove their capital adequacy. But what many organizations lack is a streamlined process that allows them to run stress tests with ease, efficiency, and control. This article outlines a five-step process that will help banks maximize their stress testing investment, making compliance easier while improving their interior management.

Current stress testing challenges

The Federal Reserve (Fed) has required banks to run the Dodd-Frank Act Stress Test (DFAST) and the Comprehensive Capital Analysis and Review (CCAR) for a number of years. While they are the Fed’s primary supervisory mechanism for assessing the capital adequacy of large banks, these exercises remain extremely complicated. CCAR and DFAST continue to cause banks to spend extensive resources – both time and money – to run the exercises, prepare the results, and respond to the findings of regulators.

DFAST tests whether banks have sufficient capital to absorb losses and support operations during adverse economic conditions (e.g., housing crash, unemployment increase, severe GDP drop, or stock market crash) while using a standardized set of capital action assumptions. (The assumptions keep each bank’s current dividend and do not include share repurchase plans.)

CCAR tests banks under similar adverse economic scenarios, but in this case regulators consider the capital action plans submitted by each bank. Under CCAR, a bank submits its proposed capital plan for the next four quarters (dividend hikes, share buybacks, etc.), and the Fed assesses whether that bank would be able to meet required capital ratios under shaky economic conditions. Put simply, can a bank afford to give dividends to shareholders if the economy starts to falter? If the answer is yes, banks then announce their capital plans to the public.

The true aim of the stress testing exercises, however, is not that banks demonstrate that they can pass tests like the severely adverse scenario per se, as no one actually expects such a scenario to come to pass, but that banks demonstrate that they have the ability to weather a storm, whatever it may be.

Over the years, there have been fewer and fewer “quantitative” stress test failures. This may be because banks are in better condition, because they have become familiar with the test, or perhaps a bit of both. Meanwhile the “qualitative” assessment has become an exceedingly important component of the regulatory program. In any case, the pressure is still on banks to demonstrate that they can manage their risks while running their businesses.

The DFAST and CCAR exercises are a part of the Fed’s effort to ensure that banks have robust processes for determining how much capital they need to maintain access to funding and continue to serve as credit intermediaries, even under stressed conditions. The most onerous test the banks must pass is called the severely adverse scenario, which features a severe recession with rising unemployment and steep declines in the stock market, housing prices, commercial real estate, and GDP.

The true aim of the stress testing exercises, however, is not that banks demonstrate that they can pass tests like the severely adverse scenario per se, as no one actually expects such a scenario to come to pass, but that banks demonstrate that they have the ability to weather a storm, whatever it may be.

The challenge for banks is to institute a process for running stress tests faster and with more control, to meet the changing demands of regulators while also improving both the process itself and the way they run the bank. CCAR applies only to the largest Bank Holding Companies (BHCs), but the challenge also applies to the next-tier banks that only need to run the DFAST exercise. While the CCAR and DFAST are US exercises, the issue is no less relevant for the banks in Europe and around the globe.

A solution to managing the stress testing process flow

There are five major areas, or components, of the stress testing process.

  1. Bringing all the data together
  2. Preparing the preliminary balance sheet forecast
  3. Conditioning the forecast with credit losses
  4. Completing the remaining calculations, capital ratios, and RWA forecasts to prepare the required reports and capital plan
  5. Overlaying a common framework for model risk management
Figure 1. Stress testing process workflow
Source: Moody's Analytics

1. Central data definitions

Source: Moody's Analytics

In the first step, all the necessary data from the various areas and business units involved in the process are brought together. The data model should support forecasting throughout the process at the most granular level, supported by multiple hierarchies and dimensions across all areas.

The data model for the process should be thought of as the single source of truth for stress testing. Organizations should take three main points into account when creating this model:

  • A central risk, finance, and treasury datamart is needed to support a large range of models and reporting requirements
  • They should leverage investment in current systems, infrastructures, and data warehouses
  • Data quality and reconciliation against production systems are important considerations

The ongoing, complex, and ever-changing regulations are pushing IT budgets at most financial institutions, requiring systems that can handle an increasing amount of data at a granular level.

Stress testing forces institutions to complement traditionally expert judgment-driven planning processes with quantitative approaches to produce forecasted cash flows.

Organizations must access, validate, and reconcile data across the enterprise. On top of the data aggregation challenges, banks need to improve the scope, accuracy, and governance of their ballooning data.

Organizations must access, validate, and reconcile data from across the enterprise, including all geographies, portfolios, and instruments, irrespective of the origin of the data. A few points to bear in mind:

  • The data model should support a repeatable, transparent, and auditable process
  • Data is not complete in each source system
  • Data is stored at different levels of granularity in different systems

These challenges are straining firm resources even further. Institutions are looking for ways to improve data quality, streamline and standardize data flows, improve the efficiency and accuracy of regulatory reporting, support validation requirements, improve auditing capabilities, and supplement management reporting. They must satisfy both the regulators and their boards about the accuracy, scalability, and sustainability of the data structure and the processes used for data management.

2. Preliminary balance sheet forecast`

Source: Moody's Analytics

The second step involves preparing the initial balance sheet forecast. With increased regulatory expectations for scenario design, banks need a process that is user friendly, as well as auditable, transparent, and repeatable. This requires:

  • Clear understanding of the key forecast drivers and their relation to the current state
  • Granular balance sheet with all jump-off data
  • A common, central source of data that allows different areas to view data in the way they are accustomed (hierarchy and dimensions)

Stress testing forces institutions to complement traditionally expert judgment-driven planning processes with quantitative approaches to produce forecasted cash flows. The approach needs to incorporate:

  • A material risk identification process
  • An effective challenge process for management and the board
  • Policies that lay out expectations for all functions involved in the capital adequacy process

Data infrastructure and system integration is a fundamental problem at most banks. Banks have been going from one short-term fix to another using SharePoint and Excel as go-betweens for multiple systems. Instead, they need a longer-term vision for how to build an infrastructure that enables effective stress testing, featuring:

  • Integration of multiple systems
  • Auditability of the results
  • Coordination across finance, treasury, and risk groups

3. Credit loss adjusted balance sheet forecast

Source: Moody's Analytics

The third component of the process takes the preliminary balance sheet forecast and adjusts it for credit losses and other forecast considerations, including Pre-Provision Net Revenue (PPNR), risk-weighted assets (RWA) for market risk, and operational risk losses.

Methodologies to project loss estimation, PPNR, and RWA are in various degrees of development. Most are housed in a range of formats (SAS, R, Matlab, and Excel, etc.), making documentation, validation, and the challenge process more difficult.

To facilitate loss-adjusted forecasting, the process needs to incorporate customizable workflow and reporting functions, including data management, auditability, and regulatory reporting. Steps in this process include:

  • Implementing a workflow that connects with banking systems to determine the role and functionality of each component in the process
  • Determining methodologies to project loss estimation, PPNR, and RWA, including documentation, validation, and the challenge process
  • Projecting losses through the bank’s asset and liability management (ALM) system for forecasted cash flows

Leveraging the bank’s current models and systems and managing the process through the workflow streamlines the stress testing and capital planning processes.

4. Capital planning and reporting

Source: Moody's Analytics

The fourth component takes the adjusted balance sheet and prepares the results to be used for the various regulatory reports, management reports, and capital plans.

Existing reporting solutions are not well suited for the complexities of data aggregation, edit checks, and management reviews needed for both CCAR and DFAST regulatory and management reporting requirements. A comprehensive solution must be capable of:

  • Handling many issues around connection points and hand-offs
  • Managing regulatory edit checks, changes, and report linkages (e.g., between 14A and 9C)
  • Supporting management reporting needs, including board and effective challenge documents

Banks need the ability to perform “what-if” and sensitivity analyses and make comparisons across forecasts to facilitate capital planning. They need to ensure the efficient alignment of financial plans, models, and forecasts across lines of business (LOBs), departments, and the board.

The process needs to facilitate capital planning and reporting by reconciling multiple sets of regulatory reports and including internal dashboards tailored to each group of end users.

5. Model risk management

Source: Moody's Analytics

Finally, the entire process should include an overlay framework for model risk management.

A common framework for managing a model throughout its life cycle is critically important as banks strive to meet deadlines from external agencies. The framework should streamline the process of creating, managing, deploying, and monitoring the bank’s analytical models, while facilitating sensitivity analysis around key assumptions and making it easier to identify sources of uncertainty.

The Fed’s 2015 CCAR Summary Instructions and Guidance document states: “BHCs are required to provide the Federal Reserve with an inventory of all models and methodologies used to estimate losses, revenues, expenses, balances, and RWAs in CCAR 2015. The inventory should start with the FR Y-14A line items and provide the list of models or methodologies used for each item under each scenario and note the status of the validation or independent review of each model or methodology (e.g., completed, in progress).”1

This suggests that the process needs to include a model risk management framework covering stress loss, revenue, and expense estimation models, which all in turn should be tailored to the task. Banks need a model risk management framework tailored to the task of stress testing at their own specific bank, not just a standardized database/document repository.

The model management framework should be repeatable and make it easy to register, validate, deploy, monitor, and retrain analytic models. As such, it should include the following capabilities:

  • Perform common model management tasks such as importing, viewing, and attaching supporting documentation
  • Facilitate the creation of a model and document repository (including model ownership, validation issue tracking, upstream/downstream models, and status of each model in the inventory)
  • Serve as a document repository with all relevant model documentation and comments
  • Track and flag issues arising in non-CCAR/DFAST models that impact the submission
  • Indicates model risk management ownership, roles, and responsibilities (e.g., validate, approve, etc.) as prescribed by regulatory guidance

A model management framework enables banks to meet the objectives set forth by the FRB, OCC, and FDIC in the same system as the process automation, thereby reducing the burden of multiple systems and allowing for consistent and tractable expert judgment overlay capture.

Building a better stress testing process

Banks need a better and faster stress testing process that can be governed with more control. The stress testing process flow outlined in this article supports the intersections between risk, finance, treasury, and regulatory compliance, while leveraging existing investments in current systems and models used for stress testing. An effective flow:

  • Provides the governance of a process that is repeatable, transparent, and auditable
  • Is a part of managing the bank
  • Allows increased frequency of stress testing
  • Leverages investment in current data warehouse, systems infrastructure, and existing models

If banks implement a similar process, their stress testing program will become more business-as-usual, freeing up valuable resources and making the entire program more accurate and efficient.


1 The Federal Reserve, Comprehensive Capital Analysis and Review 2015: Summary Instructions and Guidance, 2015.

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