Getting risk management right in an uncertain world
August 19, 2025
Key takeaways
- Financial institutions face mounting operational, climate and geopolitical risks, requiring resilient systems to safeguard operations and maintain stability.
- Advanced technologies like AI are transforming risk management by enabling faster data analysis, simulating risks and improving decision-making.
- Modernized risk management platforms and human intelligence are vital for ensuring the data quality needed to implement AI effectively.
Wherever money is at work in global markets, there are ever-multiplying risks to manage. How can advanced technologies help you stay ahead of exposure, and add strategic value?
Juggling different risks daily
Today’s investment, trading and financial services firms have a broader range of risks to manage than ever. In 2025, a single week could easily see a missile attack in the Middle East or eastern Europe, catastrophic flooding in India and the U.S., and trade tariff announcements that send shockwaves through the financial markets.
The potential impacts on companies are far-reaching, so your operations must be resilient. From geopolitical conflict and extreme weather to tariffs and financial shocks, today’s risks strike at everything from infrastructure to supply chains. Leaders need to be prepared to keep businesses up and running in every eventuality.
The fact is that you never quite know what the risk of the day will be, and which challenges will have risen to the top of your agenda when the markets open in the morning. The key is to have risk plans that you can quickly execute.
For every operational risk hazard, from military attacks to cyberattacks, you’ll need a disaster recovery or business continuity strategy. What happens if you can’t send people to the office? Or, in the case of climate risks, have you considered the impacts of a flood or a wildfire on your wider supply chain?
Then there are the financial risks to your business. How well can your balance sheet weather the effects of extreme climate events – or, for that matter, trade tensions? News of a rise in tariffs can rapidly change FX rates and create supply chain issues. So, your treasury team needs to have a firm handle on cash flows, too, and not just for monthly or quarterly reporting, but day in, day out.
In our uncertain times, modernized risk management systems and processes are increasingly critical. According to the Harmony Gap research by FIS®, based on a survey of over 500 C-suite executives and fintech decision-makers across the U.S., the U.K. and Singapore, tensions in the financial technology ecosystem cost the average organization nearly $100 million each year. Cyberthreats alone result in the biggest average annual organizational loss of $31.7 million, followed by fraud ($21.6 million), regulatory and compliance issues ($14.9 million) and operational inefficiencies ($11 million).1
Vast sums of money are at stake. And if you don’t get your risk technology right, you’ll struggle to manage the costly risks that threaten your business.
Filling the gaps in risk data with AI
Advanced technologies are already helping firms enhance their risk management capabilities. Artificial Intelligence (AI) is especially proving to be highly efficient at assembling and analyzing the vast quantities of data you need for, say, simulating climate risks or forecasting cash flows.
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AI tools can help capture and identify patterns in data more quickly, simulate potential risks and give you the visibility you need for informed decision-making. But you can’t implement these tools without the basics in place: a modernized, centralized, highly automated risk management platform that draws on a single golden source of external and internal data.
Saving a place for human intelligence
Without confidence in the completeness, accuracy and overall quality of your risk data, you can’t necessarily trust the answers or guidance that AI provides.
More than half of organizations (51%) cite data quality or lack of data as a top obstacle to implementing AI.1 And it’s in the ability to judge that quality and see potential holes in the data where human intelligence still plays a vital role.
Think about the management of cyberthreats, the biggest source of operational tension for 91% of financial services firms, compared to just 79% of nonfinancial institutions.1
When you don’t have a full picture of trends in cyberattacks across your industry, how will you know if the threats you face are typical or uncommon, and how best to manage them? Only human cybersecurity executives, working for hundreds of organizations, will have rapid access to the kind of data it takes to fill the gaps in your knowledge and complete that picture.
Using human intelligence to prepare your data for AI solves one issue for risk managers, but what about your workflow?
Since hitting the market two decades or so ago, automated workflow solutions have been subject to endless customization by organizations. Now, firms are customizing their AI tools in a way that is just as dependent on individual companies and their processes.
The problem is that every time you upgrade your technology, you must rip out your workflow and customize it all over again. With AI, it will make sense to develop and roll out repeatable solutions that are seamless to upgrade and don’t, for example, expose you to new cybersecurity risks in a future release of software.
Discover how FIS Managed Security Services can help your business
Reducing the costs of implementation
Then there’s the potentially prohibitive cost of implementing AI. Granted that, in the long term, AI can help you cut costs in terms of improving efficiency. But in the short term, high implementation and maintenance overhead are by far the biggest obstacle to adopting AI and automation in the first place for 73% of organizations.1
Of course, there are plenty of free open-source AI tools out there, which help reduce expenditure on licensing and upgrading traditional paid-for software. But what happens when you’ve outgrown your free solution?
For risk management, you need to consider the costs of managing large volumes of data, too. An open-source data lake will give you free software to help you store and access raw risk data, but you’ll still have to pay data providers for any external data that you source and rent ever more space in the public cloud to house it all.
Strengthening the balance sheet
Ensuring access to risk data is especially critical to the office of the chief financial officer (OCFO) and its ability to manage risks around liquidity and add value along the way. Again, the application of AI will help you become more proactive and strategic about risk management by allowing you to analyze more data and simulate an exhaustive range of risk scenarios and their impact on cash flow.
In a bank, the OCFO could be dealing with any number of risks to the balance sheet, from the geopolitical, climate and tariff-related events we discussed earlier to operational and employee risk and financial exposures. The sheer volume and breadth of data you need to gather and analyze in these contexts is where AI comes into its own.
Within insurance companies, the OCFO not only covers treasury and investment management, but also the actuarial risks associated with liabilities – the policies the insurer is underwriting for its customers. The challenge here is to ensure those products aren’t losing money, which requires you to closely integrate powerful actuarial modeling technology with cash management processes.
OCFOs in nonbank financial institutions, meanwhile, manage their balance sheet much like a bank, based on the different exposures they’re dealing with. Treasury and cash management are again key to mitigating these risks, and advanced technology, including AI, will play a vital role in helping nonbank financial institutions and their OCFOs maintain a clear, real-time view of their exposures and navigate a strategic path to growth.
Getting the data right first
Ultimately, however, risk visibility in capital markets relies on getting hold of the right data at the right time. So, if you’re going to invest in advanced technology to improve risk management, start with systems and processes that help you get your risk data in order.
Rather than simply building a huge, all-encompassing company database as you might have in the past, think carefully about the specific areas of risk that you need data on and whether you can source your risk data internally or need to look externally.
With a modern platform to help you process, analyze and report on that data, whether for treasury management, balance sheet management or actuarial modeling, you can then go on to consider how more advanced technologies like AI might turbocharge the process.
And if you have your data on point from the outset, you’ll stand the best chance of getting risk management right for your organization – whatever happens in the future.
1 FIS, The Harmony Gap: Finding the Financial Upside in Uncertainty (with Oxford Economics), May 2025
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