The Operational Risk Exchange (ORX) — an associative body made up by the heads of operational risk from among 100 of the world’s largest financial institutions — recently released a white paper on the use of machine learning in the management of operational risks. “We believe that the application of advanced analytics, including machine learning and artificial intelligence (AI), will be a core part of any future strategy for the management of operational and non-financial risk,” the authors indicate.

Operational risk is defined by the Basel Committee as “the risk of loss resulting from inadequate or failed internal processes, people and systems or from external events.” Such non-financial risks are, today, managed by human-intensive and time-consuming processes.

“The opportunities that machine learning techniques offer – from task optimisations and better use of resources, to cost savings and gaining deeper insights into data – are considerable, but often not used to their full potential,” the study reports.

The white paper identifies five specific opportunities for the use of machine learning to improve non-financial risk management:

  1. Freeing up valuable resources,
  2. Gaining deeper insights into data,
  3. Supporting business needs more effectively
  4. Developing greater challenge capabilities, and
  5. Benefitting from economics of scale.

 

Despite the obvious value represented in each of these opportunity sets, adoption of machine learning tools in the context of operational risk management remains slow, as firms contend with a set of internal and external challenges that inhibit experimentation with these new technologies.

But, as leading firms have discovered, “the application of machine learning and other advanced analytics holds promise to enable operational risk functions to help businesses implement strategies more intelligently, and to achieve their goals in a sustainable manner,” the report concludes.

Our customers have found this to be the case. Our Predictive Behavioral Analytics tools have helped leading firms move from hindsight to foresight in the management of culture and conduct related risks, to do more with less by allowing risk teams to operate with greater timeliness and efficiency, and to scale oversight and impact across their global footprints through the effective use of standard company data sets.

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