By Muzammil Shabudin, Risk Advisory Lead at SAS UK & Ireland.
In today’s volatile and interconnected world, businesses face an evolving landscape of risks that defy traditional predictive models. The ability to anticipate and prepare for these ‘unknown unknowns’ – risks which emerge suddenly and unpredictably – has never been more crucial.
Whether stemming from a combination of geopolitical tensions, economic instability, technological failures, or unforeseen market shifts, these risks can severely disrupt operations and erode business confidence.
Yet, traditional risk assessment frameworks often struggle to account for such unpredictability. Many rely heavily on historical data, structured models, and well-defined risk categories – an approach that works well for known risks but falls short in capturing the complex, non-linear nature of emerging threats.
To navigate this uncertainty, organisations must go beyond quantitative analysis and embrace a more intuitive, insight-driven approach to proactive risk management.
The interaction of early warning indicators
Unknown risks often begin as subtle, weak signals that may seem insignificant in isolation but, when connected, reveal emerging threats. These signals might include changes in regulatory policies, shifts in public sentiment, minor disruptions in supply chains, or early-stage cyber threats, increasingly operating simultaneously.
AI and advanced analytics provide a crucial advantage in detecting these weak signals. By processing vast amounts of unstructured data, ranging from news reports and social media trends to economic indicators and corporate filings, AI can uncover hidden patterns and correlations that human analysts might overlook, or take much longer to observe.
Cloud-native, open source enabled platforms such as SAS Viya can quickly and reliably predict possible outcomes. By leveraging powerful machine learning and analytics capabilities to uncover hidden risks and opportunities, organisations can plan future scenarios and strategies with higher degrees of confidence. AI analytics and decisioning allows businesses to move beyond reactive risk management and proactively identify potential threats before they escalate toward the risk appetite limit.
Consider a multinational company with an extensive supply chain spanning multiple regions. By leveraging AI-driven analytics, the company can monitor geopolitical developments, trade policies, and local market conditions to assess potential disruptions. If a key supplier in an emerging market shows signs of financial distress or if regulatory changes threaten critical imports, AI can flag these risks early, allowing the business to take pre-emptive action.
Simulating the impact of uncertainty
While identifying emerging risks is vital, understanding their potential impact is equally important – hence, scenario simulation. Businesses need to move beyond static risk assessments and instead simulate complex, dynamic scenarios to gauge how different variables interact under varying uncertainty.
AI-powered risk models enable organisations to stress-test different situations – from sudden regulatory changes, to cyberattacks or economic downturns. By running simulations across multiple scenarios at near-real time, businesses can explore various outcomes, understand their vulnerabilities, and develop contingency plans accordingly. It can also lead to unforeseen business opportunities.
Take again the growing risk of geopolitical tensions affecting trade routes. An FS firm operating internationally might simulate the impact of trade restrictions on currency fluctuations, interest rates, and investment portfolios. By assessing different scenarios, the firm can devise strategies to hedge against currency risks and adjust its investment approach, preparing its clients for potential market shifts, reducing risk exposure and increasing resulting returns.
Building adaptive strategies for resilience
Recognising risks and simulating their impact is only the first step – businesses must also develop adaptive strategies that enhance their resilience. This requires a cultural shift in how organisations approach risk appetite management, moving from rigid frameworks to dynamic, data-driven decision-making.
One effective approach is embedding AI-driven risk intelligence into boardroom discussions and strategic planning. By equipping leadership teams with near real-time insights via predictive analytics, organisations can make more informed decisions and respond to emerging risks with agility. Instead of waiting for a crisis to unfold, companies can proactively adjust their strategies to mitigate potential disruptions.
Furthermore, fostering a risk-aware culture across the organisation is essential. Risk management should not be confined to a specific department – it should be an integral part of the business mindset. Employees across the Three Lines of Defence should be trained to recognise early warning signs and contribute to a proactive risk culture – the bedrock of operational resilience.
A clearer picture for boards and decision-makers
One of the most significant advantages of AI-driven risk assessment is its ability to provide boards and executive teams with a clearer, more comprehensive risk assessment. Traditional risk reports often focus on well-known threats, but they fail to capture the blind spots that could pose significant dangers.
By integrating AI-powered insights into board discussions, businesses can shift from a retrospective view of risk to a forward-looking “what-if” perspective. Instead of relying solely on past performance metrics, decision-makers can leverage real-time data, scenario analysis, and predictive models to navigate uncertainty with confidence.
For example, AI can help identify potential systemic risks in the FS sector by analysing cross-border financial transactions, regulatory changes, and economic indicators, in some cases simultaneously under varying assumed scenarios. This allows board members to make more informed decisions about investment strategies, regulatory compliance and operational resilience.
Combining insight with intuition – the new endgame
In an era of constant disruption, businesses can no longer afford to rely solely on historical data and traditional risk models. The ability to detect weak signals, simulate complex scenarios, and build adaptive strategies is key to staying ahead of emerging threats.
By leveraging AI and advanced analytics, organisations can move beyond reactive risk management and embrace a proactive, intelligence-driven approach. This not only strengthens business resilience but also empowers leadership teams to make informed decisions with confidence.
In the face of the unknown, the combination of AI-powered insight and intuition will be the defining factor that separates businesses that thrive from those that struggle to survive.
