For risk appetite, it pays to think more strategically This is the fifth blog in a series of seven about the ways in which frequently used shortcuts can deliver poor outcomes for operational risk management teams within financial services firms.
How speed can be the enemy of usefulness when it comes to reporting This is the last in a series of four blogs about the ways in which common shortcuts can undermine core operational risk elements within financial services firms…..more
Ways in which some loss event approaches can be false economies This is the third in a series of four blogs about the ways in which common shortcuts can undermine operational risk management success within financial services firms. You can view the other blogs here: The Shortcuts Trap
This is the second in a series of four blogs about the ways in which common shortcuts can undermine overall operational risk management success within organizations.
How RCSA timesavers could increase risk within the business This is the first in a series of four blogs about the ways in which common shortcuts can undermine overall risk management success within organizations. You can view the second blog
Developing a cost-benefit approach to risk and control frameworks has long been a goal for operational risk teams. Boards, senior executives, and the business could use such analysis to make better decisions about where to invest in their control frameworks.
In most financial services organizations, operational risk data is underused. Vast amounts of operational risk data – including operational risk loss event data – is often collected but are not transformed into meaningful reports for key stakeholders. As a result,
Much ink is currently being spilled about Big Data, artificial intelligence (AI) and machine learning (ML) within the operational risk discipline. In time, certainly, these technological approaches to processing operational risk data will have a role to play. However, today’s
Amidst all of the hullabaloo about artificial intelligence (AI), machine learning (ML) and other new technological approaches to operational risk, practitioners of the discipline should pause and reflect. What are they doing with the operational risk data they are collecting
Although it’s been more than a decade since financial services firms have had to undertake stress tests and scenario analysis for operational risk, regular errors in both approach and execution continue to be made. Below are 7 of the top