Taking and managing risk in the age of AI

My first column about “risk in the digital world” highlighted some general points on how we can create value in the digital age. In this second column, I discuss how Risk practitioners can help organisations leverage the opportunities presented by Artificial Intelligence (AI) and the automation age.

AI and process automation is an integral part of the digital age. Across many industries and sectors, robots and computers are automating a range of work more accurately, faster and at lower cost than people can (or used to) do. The way we manage operational risk is changing as a result. Whilst automation is achieving a reduction in errors and improvements in productivity and safety, people still have a crucial role in managing operational environments. The analysis of data and outputs from algorithms from AI and process automation is a new frontier for the Risk profession to create value for organisations. It can lead a step change in how people use risk management to make a difference.

What does AI and automation mean for the risk management profession?

Over the coming years, I think we will see an increasing demand for “risk intelligence and data analytics” skills in advertised Risk jobs. People in the Risk profession should be working on gaining these skills. If you can demonstrate real-world use of AI and technology to create value and see insights to take and manage risk, you will gain a competitive advantage (for now) in the job market.

Risk practitioners should be helping their business teams, from Executives to the frontline workforce, to ensure their digital strategy leverages AI and process automation to see insights into previously unseen risks, whilst ensuring appropriate resilience is in place to manage Cyber risk. Established techniques such as risk appetite principles, what if analysis and scenario planning can be used with data analysis for powerful results.

Consider a couple of examples – the finance and automotive sectors – of how Risk practitioners can help create value by being embedded in business decision-making.

Financial businesses are using AI and automation in various ways, from risk-assessed trading through to consumer banking (kiosks, customer service etc.). Risk practitioners in financial organisations are already working with their business lines to develop data-driven “What if” scenarios to determine new insights and new levels of ability to take and manage risk, with new and innovative controls to achieve competitive advantage.

Automotive manufacturers are busy working out how to use AI for self-driving road vehicles. Risk practitioners can be integral to an automotive manufacturer’s digital business strategy. Their input can range from strategic risk profiling to manage the risk of introducing AI to its product range, to helping to design appropriate controls to manage data privacy and Cyber risk, and working with engineers to review What If? scenarios to ensure all aspects of AI are considered. Risk practitioners can also help servicing and roadside assistance departments to use risk appetite thresholds for vehicle parts performance, designing alert thresholds about potential faults and problems. At a broader transportation level, Risk practitioners can help the bodies that own and operate transport infrastructure to use data to reduce the risk of traffic congestion and to improve safety.

In my first column I talked about how risk management techniques in many (not all) industries are performed in a mostly qualitative manner. With the digitisation of business and commerce, and the use of AI and automation, this is changing. Whilst qualitative risk reviews are still important, we are getting better at using data to take and manage risk. This is a trend that will surely continue as we progress into the future and it is one that Risk professionals need to embrace.