Generative AI's Impact on Risk Jobs: A Q&A with Michelle Liposky

While generative AI is viewed by many financial institutions as a highly leverageable business opportunity, finding the right professionals to help them understand the breadth of its associated risks has proved extremely challenging.

Ninety-three percent of companies recognize the hazards tied to generative AI, according to a global survey from risk software company Riskonnect. However, just 9% say they’re prepared to manage these threats and only 17% of risk and compliance leaders have formally trained or briefed their organizations on the risks of using generative AI.

Michelle Liposky, Managing Director and Head of Business Risk and Solutions, BMO Wealth Management

Given this strong gap between need and skill, it would be advantageous of entry-level risk managers to embrace generative AI as a potential professional niche — and not as a tool that may someday render their job redundant, says Michelle Liposky, managing director and head of business risk and solutions at BMO Wealth Management. "Technological advancement has made risk management more important and more dynamic than ever before," she says. “I urge young financial risk managers to not be afraid, as I believe human and machine collaborations will lead to more opportunities, not fewer.”

To learn more about the present and future of generative AI, as well as how prospective and entry-level risk managers can leverage this new frontier, we spoke with Liposky, who currently specializes in integrating data with machine learning and AI.

Where do you see the future of generative AI headed?

AI, including generative AI, will continue to be a game-changer across industries. We’re just scratching the surface of what it can do on both the revenue generating and risk management sides of business. As we better understand generative AI’s capabilities and how to manage the risks associated with them, we’ll continue to test and learn and narrow its application to use cases that have a positive cost-benefit and acceptable risk-reward. 

How can a risk team begin the process of implementing AI tools into their current risk processes?

Start by understanding the “what.” What risks are we taking, what controls are in place, and what tells you those controls are working?

Subsequently, address the “how.” How do we know the risks we’re taking, how do we design and identify controls, and how do we know that they’re working?

Lastly, think about the most efficient way to embed and sustain the “what” and “how.” What processes are required? What activities require judgement, interpersonal interactions and discussions between humans, compared to what can be done by a machine?

In what areas of risk and finance are you most prominently seeing the implementation of generative AI?

Currently, the industry is testing and learning the use of generative AI for application across revenue generating, client engagement, and operational areas, with the earliest adoption likely coming in relatively less risky areas that create capacity. An example of such a less risky area is Q&A solutions — where AI users ask questions about past-due files and get a response in a tabular format. Users can also prompt generative AI to create an automated customer summary, saving time and effort and manual cut-and-paste between sources of data.

Should young financial risk managers be concerned about generative AI rendering their jobs irrelevant or redundant?

No. Blending humans with machines leads to more opportunities, not fewer.

While risk professionals of the past spent quite a bit of time on framework activities that occurred on a regular cadence, risk professionals of the future will be involved on an ongoing basis throughout the product lifecycle – and will be expected to offer more critical, as opposed to mathematical, thinking. Ultimately, the work of risk professionals will be more stimulating and valuable than ever before, because it will be about getting ahead of risks instead of reacting to them. 

How is generative AI changing what's expected out of entry-level risk professionals, and what new skills and abilities are firms seeking from prospective hires?

Entry-level professionals would benefit from starting with fundamentals and building upon that foundation to layer in the use of advanced technologies like generative AI. At the same time, while advanced technologies are leading to different expectations compared to years past for both new and experienced risk professionals, the most sought-after skills and abilities haven’t completely changed. For example, critical thinking is among the most important skills that entry-level risk professionals can have — along with possessing a positive attitude, the desire to learn, and a team focus.              

It also goes back to understanding where a business is going, knowing what risks the business is taking, and managing those risks. It’s not enough to look at key risk metrics; one must understand how they tie back to the business. To make that connection, one must think critically to understand the interconnectedness among risk types, the likelihood and impact of risk events, and how to get ahead of them. 

There’s no magic formula. One must learn, think and ask questions, over and over again.