Across the securities and investments industry, firms are investigating opportunities to harness the power of artificial intelligence (AI). And one of the top use cases they’ve found for AI tools is risk management.

Both AI and risk management are broad topics, so it’s good to see securities and investment firms taking full advantage of AI’s versatility and applying its different forms to an ever-growing range of risks.

At the same time, firms are exercising caution, because AI tools bring their own risks.

In the wrong hands, for example, AI can spread disinformation and compromise security. Without the right technology skills, a business may not get the best from it. Or, at worst, these problems could even put you out of a job.

It's with an eye on the threats, as well as the benefits, that we now see securities and investment firms adopting – or considering adopting – AI to tackle a variety of risk management challenges.

For me, some of the most interesting use cases in the industry include:

1. Informing and analyzing trading decisions

By rapidly absorbing and querying the details of different instruments and their attributes, AI models can make sense of the most complex investment portfolios and provide a faster, deeper and clearer picture than ever of their performance.

With daily or intraday automated reports, driven by AI, the front office has all the information it needs to plan trading activity most effectively and make optimal decisions.

2. Assessing climate risks

For strategic financial planning and near-term regulatory reporting, it’s increasingly critical for securities and investment firms to know the impact of climate change on their investments, assets, reputation, supply chain and business as a whole.

Specifically, firms face two main types of climate risk: physical risks from extreme weather events or chronic changes (e.g., to sea levels or temperatures) and transition risks from the move to a low-carbon economy.

In each case, AI technologies, like machine learning and natural language processing, are helping securities and investment firms make projections and mitigate the risks by gathering, unpacking and analyzing vast amounts of intricate geographical data under multiple environmental and business scenarios.

3. Building an enterprise risk management framework

For an enterprisewide view of all the risks they face, most securities and investment firms have spent the last 15 years or so learning how to collect high-quality risk data on everything from internal losses to external credit risk events. But again, there are large, if not excessive, volumes of data to unpack.

AI is the logical next step for enterprise risk managers with almost too much data to make sense of. With AI tools taking the strain, they can accelerate trend analysis and report far more efficiently on capital or operational risks.

4. Cleaning up data

To get the most from your data with AI, you need to get the data in order first. Luckily, the curation, transformation and reconciliation of data is something that eagle-eyed AI tools can assist with too.

Once AI has helped extract the right data points, strip out the rest and build the best possible data model. The data is much easier to work on, even for individuals with limited real-world experience of managing risk. That makes AI an ideal proposition for smaller organizations without a big IT team that are looking to recruit and train tech-savvy young talent.

5. Reducing key-person dependency

Speaking of talent, advanced AI tools could also help minimize the reliance that some firms develop on certain members of staff, especially those that have helped code and run legacy software.

We’re now seeing that generative AI can help share expert knowledge and educate others much faster on unfamiliar systems. If you’re in doubt about a business process, just ask a gen AI chatbot for the answer.

The downside, of course, is the potential long-term threat to job security, which makes it crucial to cross-skill staff in a wider variety of tasks and activities.

6. Creating content

Risk managers traditionally spend most of their time transforming data into information and only the minority probing the output. With its ability to produce coherent content from big pools of data, generative AI could shift the balance in the opposite direction.

While most firms are still trying to understand all the possibilities – and dangers – of generative AI, it might ultimately help securities and investment firms focus more of their efforts on analytical work that adds the most value.

How will you use AI?

Use of AI in the business world is becoming less a question of “if” than “when.” In October 2023, a poll of around 160 securities and investment firms at a Risk.net webinar found that 69% are currently exploring how to use AI in production, with 93% either using or considering use of AI tools in some shape or form.

Concerns remain about the potential impact of AI on staff and security, and as securities and investment firms incorporate new tools into their operations, there are change management risks to negotiate, as well.

But with the strategies and skills in place to mitigate risk exposure and make the most of AI, the industry can continue to explore the latest technologies and find confident solutions to its challenges.

Want to learn more?

• Listen to our latest webinar with risk.net "Empowering risk management with AI".
• Learn more about our securities and investments solutions.