Within the past 15 years, regulatory bodies such as the Federal Reserve Board (FRB) and the Office of the Comptroller of the Currency (OCC) have classified financial institutions as models or tools used to predict material or material information. We have developed standards that must be followed. This information may include losses, value at risk (VaR), liquidity needs, valuations, etc. The basis for these standards is Supervisory Letter SR 11-7: Guidance on Model Risk Management and OCC 11-12: Sound Practices in Model Risk Management. .
Since then, there have been many oversight letters and rulemakings to address data quality. for example, CFO Certification, BCBS 239, and other controls, infrastructure, and scenario design that provide input to these models. What these standards seek to accomplish is to ensure that the tools used to quantify information important to decision-making follow standard protocols of development rigor, governance, and validation. These standards have been adopted for use in comprehensive capital analysis and review (CCAR), business-as-usual (BAU) capital planning, and general risk management, but as financial institutions respond to the next wave of supervision, There are several areas where you need to invest. Expectations.
Need for improved recovery and resolution planning capabilities
The Federal Deposit Insurance Corporation’s (FDIC) proposed new Insured Depository Institution (IDI) resolution planning rules create a gap in the industry regarding the level of sophistication, flexibility, and dynamism required for models and other supporting infrastructure and processes. Questions have arisen. Given the recent examiner focus on whether financial institutions can produce reliable resolution liquidity execution needs (RLEN) forecasts within days given certain inputs. And these questions are more than justified. This is just one example of regulators’ increased expectations and emphasis on speed when it comes to testing and vetting financial institutions’ solutions for critical infrastructure.
Another example in the rehabilitation resolution planning (RRP) space is the acquisition of valuable franchises to support multi-buyer resolution strategies for Group A financial institutions, as defined in the proposed IDI resolution planning rules. Possibility to split components. Many IDIs still employ a single-buyer resolution strategy, which assumes the entire organization will be acquired in his one transaction. As a result, their technology may not have the ability to spin off a deposit franchise or portion thereof, for example, for quick sale to another financial institution that is only interested in a particular region or business segment. there is. However, according to the proposed rule, organizations worth more than $100 billion must have the ability to segment the organization by franchise element to accommodate multi-buyer marketing strategies. Additionally, the ability to quickly evaluate these franchises and demonstrate to the FDIC that the methodology for dividing the organization achieves the objective of maximizing value is limited by the potential for this information to be produced in a very short period of time. It’s not an easy task considering that. (Solution weekend, etc.).
Adhering to data quality standards and having the ability to generate data in a timely manner, whether for upstream predictions at the model level or downstream in a virtual warehouse, are included in the proposed IDI resolution planning rules. This is a basic requirement for all features provided. data room. Liquidity may have the necessary data quality given its maturity from a regulatory perspective, but new requirements such as being able to carve out valuable franchises are forcing corporations, businesses and organizations to Additional work may be required to provide a more flexible view of the organization beyond. Important operations. Examples of this include wealth management and deposit franchises. Some remediation work may be required on your system of record (SOR) or subledger, but given that some of this infrastructure is old, the limitations of older technology can pose challenges. Financial institutions must expect much longer lead times and higher investments for work related to older technology to produce the desired results.
It’s clear that speed is a common theme in the latest proposals, but so is the ability to anticipate different scenarios and assumptions. While companies can leverage a number of stress testing infrastructures and scenarios as a base, being able to induce the failure of a financial institution through idiosyncratic scenarios means that companies can understand where their more critical vulnerabilities lie. It shows that you are doing it. It is also wise to consider second-order and even third-order effects and demonstrate their capabilities.
Governance and management are also important topics to address. The current process for forecasting relies on a lengthy process of data certification, assumption scrutiny, review and challenge, and board approval. Forecasting, planning, and analysis (FP&A) and CCAR are notable examples. However, organizations need to be realistic about how long it will take to generate and certify results. Baseline projections may give you the luxury of weeks to certify results, but the FDIC is working with a compressed resolution runway, so it takes some time to certify results in a matter of days. may be required. The use of automation and specific playbooks that account for compressed decision windows should be adopted and rigorously tested to reduce non-value-added activities that only increase the time needed to make critical decisions. .
Build a foundation for success
Given the current state of many RRP programs, increasing execution speed without investing in the data quality, technology, and controls that underlie predictive and evaluation capabilities will inevitably lead to increased operational risk. Although these programs take years to complete, companies invest in the necessary foundational capabilities that can be steadily built and may be attractive in the short term (but subject to further regulatory oversight in the long term). ) cost-saving measures should be resisted. semester). Additionally, these features should be tested regularly to ensure they are consistent with supervisor expectations.
Contact our experts for more information. Forvis.
