While the pandemic certainly pulled into focus the need for resilient systems across financial services, the push toward financial resilience was first born in response to the 2008 financial crisis.
Since 2008, focus has shifted toward building resilience across operations in the financial services sector, by assessing vital business functions, setting levels of tolerance that these functions can withstand, and testing the tolerances at regular intervals.
The Basel Committee on Banking Supervision defines operational resilience as the ability of a bank to deliver critical operations through disruption. This ability enables a bank to:
- Identify and protect itself from threats and potential failures;
- Respond and adapt to – as well as recover and learn from – disruptive events.
Unlike typical risk management or more traditional compliance-based approaches, when it comes to operational resilience, banks should assume that disruptions will occur – and consider their overall risk appetite and tolerance for disruption. In the context of operational resilience, the Committee defines tolerance for disruption as the level of disruption from any type of operational risk a bank is willing to accept, given a range of severe but plausible scenarios.
While the ability to predict which areas are likely to cause disruptions was once the purview of a human supervisor, given the shift to digital operations, it is only logical that firms employ tools such as artificial intelligence (AI) and machine learning (ML) to identify patterns and risks within an institutions’ complex technology systems.
This Finextra impact study, in association with BMC, outlines five key considerations that financial institutions must be aware of, ahead of impending regulatory deadlines, as well as the technology-based solutions available to assist them in building a robust and compliant operational resilience strategy.