In November 2011, the Federal Reserve adopted the capital plan rule, which requires bank holding companies (BHC) with consolidated assets $50 billion or more submit annual capital plans to the Federal Reserve (The Fed) for review. The asset size is measured over the previous four calendar quarters as reported on the Consolidated Financial Statements for Bank Holding Companies (FRY-9C) regulatory report. The Federal Reserve’s (The Fed) annual Comprehensive Capital Analysis and Review (CCAR) is an intensive assessment of the capital adequacy of large, complex US bank holding companies (BHC) and the practices of these BHCs use to manage their capital. The Fed reviews BHCs on both a qualitative (several stress test scenarios) and quantitative assessment (supporting practices and capital planning). The Dodd Frank Act Stress Test (DFAST) is used by regulators to assess small community banks.
Fundamentals of CCAR AND DFAST1
CCAR/DFAST – Reporting for both FR Y-9C (consolidated) and FR Y-14A/Q/M
There are distinct differences between the Comprehensive Capital Analysis and Review (CCAR) and the Dodd-Frank Act Stress Test (DFAST). CCAR is used by the regulators to assess large BHCs while DFAST is used by regulators to assess small community banks. Outlined below are differences:
CCAR – Large BHCs:
- Semi-annual submission (January and July)
- Submission of reporting form FR Y-14A
- Must incorporate U.S. Basel III capital framework in capital projections
- Tier 1 common ratio is calculated using existing capital rules
DFAST – Small Banks:
- Annual submission by March 31st of each year
- Report on form FR Y‐16
- Annual public disclosure of summary results beginning in June 2015
- Not required to incorporate U.S. Basel III capital framework in capital projections until the 2015 stress testing cycle starting in October 2014
- Not required to calculate Tier 1 common ratio for 2014 stress testing cycle
Key Regulatory Challenges and Solutions for CCAR and DFAST
The section below provides examples of specific challenges that arise from CCAR and DFAST along with how native visual analytics address those challenges. Native visual analytics describes the process of running business analytics engines directly where the data resides, allowing subject matter experts to join and render that analysis visually and in a self-serve manner. Data is not aggregated and moved to another place for analysis so that data can be evaluated at a very granular level.
|Regulatory Challenge||Enablement via Visual Analytics Capabilities|
|Data Aggregation and Management: Both CCAR and DFAST processes require both large and small banks to manage and adequately assess data from first line of business (LOB) to back-end users. The management and aggregation of data varies immensely across financial institutions as some firms are automating the process, thus integrating between data providers and data users. Nonetheless, there remains much room for firms to improve their data systems and operations.||Efficiency and efficacy are increased when data users are empowered with self-service and direct-to-data analytics that enable collaboration across lines of business. A subject matter expert for one line of business can benefit from the bigger picture knowledge of operations and vice versa. For example, analysts with limited data access can share their view directly with operations. Operations, with access to more data, can expand on the initial work to uncover deeper and practical insight.|
|Accuracy and Timeliness of FR Y-14 and FR Y-16 Reporting: Both small and large banks continue to struggle with improving both data validation and oversight. This could be a result of a still greater use of manual entries and data manipulation based on spreadsheets, files, and data warehouses that may not be fully integrated but is pulled from various sources. This is an apparent problem in reconciling data templates on the FR Y-9C to the FR Y-14 reports.||Data quality is improved when subject matter experts can evaluate the same version of data regardless of type. Native visual analytics is a business intelligence solution that allows lines of business (data providers), data analysis, and regulatory reporting functions to suffice their particular requirements in a self-service fashion without manipulating the underlying data for their own ends.|
|Aligning Basel III and Stress Testing: BHCs and banks over $50 billion would need to incorporate the Basel III framework into their capital estimates. This may present some challenges in forecasting elements of the Basel III capital adequacy ratios for baseline and stressed scenarios. BHCs and banks would need to take additional steps in valuating and forecasting unrealized gains and losses on available-for-sale (AFS) and held-to-maturity (HTM) securities experiencing non-temporary impairments on capital. This also includes forecasting deferred tax assets (DTAs), mortgage servicing assets net of deferred tax liabilities (DTLs), and calculating risk weighted assets (RWAs) exposures for transactions and derivatives, to name a few.||Native visual analytics is used to evaluate any type of data so that sources can be plugged in as needed to see how they align (or don’t align) regardless of the original purpose of that data source. Real-time analysis juxtaposed with historical evaluations greatly enhances a firm’s ability to monitor, measure, and report changes on adequacy ratios for both forecasting elements of Basel III capital and baseline and stressed scenarios.|
Native visual analytics unifies data discovery, business intelligence, and real-time visualization in a single, integrated platform. It provides users with direct access to big data (it runs natively on Hadoop clusters to leverage its capabilities) through an intuitive and self-service interface. Referring to the examples above, a team can explore billions of rows of highly varied data, join views into interactive packages, and share that package with relevant colleagues to solve problems in a timely, secure, and collaborative way.