Outlining a CCAR Strategy Beyond Model Development

Much of the discussion around CCAR has been focused around the scenario modeling required by the regulators. While I agree scenarios are a major part of the tests, they only represent one half of the picture. The other half of course is related to Data.

Scenario modeling is a complex process and requires banks to assemble teams of consultants, data scientists, PHDs, and economists to debate and develop various aspects of the regulatory requirements. While this is not an easy task, most organizations are able to quickly build or buy the necessary resources to accomplish this task.

Sourcing high quality Data on the other hand is not just an intellectual exercise, and requires varying stakeholders to work in concert, and in most cases ends up being the larger challenge for CCAR institutions.

The equation for CCAR is as follows

Quality Data + Quality Models = Accurate Scenario modeling

So how does one source quality data from within the organization? This question is often asked, and rarely has an easy answer. The ability of a bank holding company (BHC) to source high quality data is dependent on sound Data Governance practices. As with most other types of businesses, most CCAR banks have spent the past two decades defining and improving Critical Business Processes (CBPs). These activities have yielded significant efficiencies in day-to-day operations and have led to advanced and complex products and services for clients and customers.

As banks are brought into the CCAR fold, and the regulators become more intelligent around Data Governance (DG), most banks are scrambling to establish robust DG programs. But unlike models, the effort required to implement DG programs is many fold larger, more complex, and requires massive organizational collaboration.

For the most part, defining Critical Data Elements (CDEs) and creating governance mechanisms around this data set has been an after thought, if not completely neglected by some.

Unlike modeling, throwing resources at the DG program can have diminishing returns, especially if the organizational support is not explicitly in place. Most organizational data is hosted in silos and as such falls victim to the standard territorial conflicts. How many of us have sat through meetings and have heard both business and IT teams refer to such structured data as "My Data".

In my experience, the My Data problem is likely to derail or delay enterprise and regulatory initiatives such as CCAR.

To implement an effective Data Governance and Data Quality programs, there are three main work streams that need to be managed concurrently ( I will cover each of these streams in a separate post at a later date)

1. Definition and approval of company wide data policies e.g. Data Ownership, Critical Data Elements and Data Quality
2. Adoption of standardized messaging standards e.g. ISO 20022
3. Tools adoption e.g. Business Glossary, Data Profiling and Lineage, Quality etc.

As long as Data Governance and Data Quality are considered an after thought instead of precursors, the CCAR programs will continue to deliver abysmal results and regulators will continue to hand out MRAs.