It’s been nearly a decade since the financial crisis of 2008. However, the Comprehensive Capital Analysis and Review continues to be an ever-evolving process. Trends for 2017 and 2018 show that the Fed continues to work toward establishing clear and unwavering CCAR standards.
The year in review
For the third year in a row, every bank passed the quantitative stress test assessment. For the first time, each of the 13 SR 15-18 banks (large and complex firms) passed the qualitative assessment. Mid-sized banks (10bn – 50bn in assets) are now required to comply with DFA stress testing but not yet CCAR.
More institutions turned to enterprise solutions like AxiomaSL for assistance with CCAR regulatory compliance, data management and capital adequacy assessment in response to the need to move from silo-based data sets to streamlined and integrated financial, risk and reference data and automation. Model validation remains a critical requirement for CCAR stress testing and an ongoing challenge for risk managers, with some building in-house models.
Overall, banks were better capitalized than in previous years, with an increase of 1.87 percent over 2016. Clarifying and strengthening CCAR processes such as risk identification, governance and control areas, including model risk management, data accuracy and internal audit, remained a top priority for the Fed.
The current administration has expressed a strong interest in deregulating the financial and banking industry. With this in mind, banks are trying to determine how much effort and resources to allocate toward CCAR in 2018. However, several trends are expected to dominate in the coming year.
The Fed will remain committed to upholding the standards of CCAR and increasing its transparency, even if the overall process itself becomes less complex or less frequent. Likewise, according to McKinsey, there are no big changes on the horizon for SR 15-18 banks in 2018.
In light of macroeconomic uncertainties and the increased use of digital financial products, the Fed is pushing for banks to focus on new types of stresses. The Fed has also called for increased internal controls.
Data accuracy is a big part of stress analysis and internal controls. Firms could improve their CCAR performance by implementing machine learning to improve data accuracy, the predictive capabilities of risk models, build primary models and improve the validity of data used to determine stress test baselines. As the Fed will expect all firms to have mastered risk management and modeling by 2018, machine learning could help firms meet this requirement.
While the future of banking regulation, in general, may be uncertain, it is clear that the Fed remains committed to maintaining and enforcing current CCAR protocols.