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The future of operational risk management

Evolving data architectures

Moving beyond traditional operational risk data models to more integrated data structures for early risk identification, remediation and value creation.

In a previous paper The future of operational risk in financial services, we highlighted how cost efficiency was becoming a higher priority in risk management and compliance. We also showed the consequent pressures on risk leaders to explore and embrace new technologies and techniques that can help improve the efficacy and effectiveness of their programs. We introduced concepts such as predictive risk intelligence and the use of advanced analytics for pattern recognition, as well as correlation and causal analysis to give operational risk managers a head start on identifying the buildup of potential risk and the need for remedial action.

Since our original publication in March 2018, we have seen only greater moves toward predictive risk intelligence. Globally, more banks are trying to make their operational risk management programs more forward looking. The purpose of this follow-up point of view is to highlight one of the implementation challenges to actualising a more predictive operational risk management program. That challenge is the need for the evolution of the data architecture and models.

Published: March 2019

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