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As risk management becomes increasingly data-driven, generative AI is emerging as a game-changing technology for risk teams. This is great news for you as a risk professional because it enables you to automate repetitive tasks so you can focus on higher-value analysis and strategic advising.
In this article, we explore 5 key applications of AI for transforming risk management, and how you can best use these tools to streamline your processes.
Staying current with changing laws, regulations, and standards is essential for risk teams to maintain compliance. However, as a risk professional, you know that manually reviewing endless pages of rules and requirements is tedious and time-consuming.
Generative AI can help you accelerate this process by automatically reviewing new regulations and providing summarized analysis.
For example, when a new industry cybersecurity framework is released, a you could have the AI the entire text of the framework and summarize it into a brief. This would then report insights including new regulations and requirements added, how they change from previous standards, and new reporting obligations.
This would allow you to quickly understand the most important implications of the long, complex new regulation, giving you the basics so you can analyze how to apply the insight.
This approach can be leveraged anytime new laws or rules are released to accelerate compliance analysis. The AI does the heavy lifting of reading and summarizing regulations so risk teams can focus on strategic analysis and advice.
MORE: What the Kroll Report means for your business
Assessing potential risks associated with vendors, mergers and acquisitions, joint venture partners, and other third parties is a crucial but manual process for risk teams.
You need to be on top of all types of risk, not just one category, which adds to your research time. Generative AI can help you automate the collection and analysis of third-party risk data across several categories, including:
Rather than manually researching all these areas, you can provide prompts to AI like:
"Analyze available data on potential acquisition target Company X and summarize key risks identified across financial, operational, strategic, compliance and reputational dimensions in a 2-page brief."
The AI would rapidly gather and assess available data on the target across the specified risk areas to accelerate due diligence. This gives you a quick overview of third-party risk factors to help you make strategic partnership suggestions even faster.
MORE: Third-party risk checklist for compliance officers
You need to understand how emerging risks like technology disruptions, climate change, supply chain shocks, or geopolitical tensions could quantitatively impact your business. With the rapid pace of change, these risks can be difficult to model.
Generative AI enables you to automate scenario modeling and “what-if” simulations to gain data-driven insights faster.
For example, to assess potential supply chain disruption, you could prompt the AI:
"Using our financial data, create a model to simulate the potential effects of a 6-month supply chain disruption from one of our major manufacturing partners. Estimate the impacts across revenue, costs, lost production time, and effects on key clients."
The AI would rapidly digest available data on the company’s finances, operations, and partnerships. It could generate a report estimating potential revenue loss from delays, extra costs from expediting shipping, possible lost production days, and how that will impact your clients.
While approximations, these quantitative insights inform risk mitigation plans. The AI can also run various scenarios on command to quantify hypothetical impacts across the business, making it easy for you to plan for a number of possible scenarios.
With this approach, you save significant time because the AI provides a starting point for further analysis on the most effective ways to mitigate emerging risks and strengthen resilience.
MORE: Due diligence checklist
Organizations generate massive amounts of risk data cross systems, tools, and business units. This includes security logs, incident reports, audit findings, threat intel feeds, vulnerability scans, policy violations, and more. Manually processing this vast, disparate data to uncover correlations, trends, and emerging risks is extremely difficult. AI can help you automate the analysis to identify signals that may have been missed.
For example, you could ask your AI tool to analyze the last years’ worth of security logs, incident reports, vulnerability scans, and external threat data to identify top risk exposure and have it summarize the top-5 findings.
The AI would correlate and interpret patterns across these complex data sets that human analysts would likely miss. The AI may uncover that:
These synthesized insights help focus your team’s efforts on the most urgent risk areas, augmenting the power of your analysis.
When applied responsibly, generative AI enables risk teams to work smarter and faster by automating repetitive tasks. This allows more time for identifying emerging risks and advising the business. As the technology advances, AI will become an invaluable asset for strengthening risk management.
For more information on the future of Generative AI, we invite you to view our LexisNexis® Future of Work Report 2024: How Generative AI is Shaping the Future of Work to explore more of ways Generative AI is changing the landscape.