Executive Summary
On Call International explains how predictive analytics is transforming travel risk management (TRM) from a reactive process into a proactive strategy. By combining real-time data, machine learning, and historical trend analysis, organizations can identify emerging threats earlier, improve traveler safety, and strengthen business continuity.
Key takeaways:
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- Predictive analytics helps identify travel risks before they becomes crises.
- Real-time data and AI improve risk forecasting and decision-making.
- Organizations can better anticipate disaster impacts and recovery challenges.
- NLP tools help detect early signs of civil unrest and potential disruptions.
- Scenario planning and predictive intelligence are becoming essential for modern travel risk management.
The days of waiting for a crisis to strike and scrambling to respond could soon be over. With sharper data and smarter analytics, organizations can now see risks forming long before their people are affected, marking a distinct shift from reactive crisis management to proactive, data-driven travel risk mitigation.
By leveraging real-time data, machine learning, and historical pattern recognition, predictive analytics allows organizations to forecast emerging risks, and by embedding this insight into their travel risk management (TRM) frameworks, they can anticipate disruptions and tailor their responses more effectively. And as predictive analytics continues to develop, it is becoming an indispensable tool to deliver traveler safety and business continuity.
There are several potential risks to safety and security in TRM, including, but not limited to, crime, political and social unrest, terrorism, gender-based violence, and natural disasters. While the traditional approach to TRM has equipped travelers with plans on how to respond to an event, predictive analytics helps them stay ahead of emerging risks before they develop into a crisis.
What are Predictive Analytics and How Do They Work?
Good data lies at the heart of strong predictive analytics and it is sourced from official travel advisories, global media, social media, and economic or political indicators. Machine learning tools identify patterns in this data and diagnose the root causes of emerging risks, helping risk managers to determine their likely trajectory and impact.
For example, when preparing for a hurricane, predictive analytics goes beyond providing a weather forecast. It incorporates historical storm data for the region, evaluates the local government’s response capacity, assesses the socio-economic conditions of affected populations, and examines the resilience of critical infrastructure. This broad perspective of risk allows travelers to predict the kind of conditions they are likely to face before, during, and after the disaster.
This provides travelers with the practical insight they need to calculate whether it is best to shelter in place or proceed with evacuation, and if evacuation is not possible, they will be equipped with a better understanding of the timeframe of disaster relief and how their exposure can be limited.
How Predictive Analytics are Reshaping TRM
In September 2024, two Category 3 hurricanes – John (Mexico) and Helene (Florida) – struck the southern part of North America. The preparation for, impact of, and response to these respective storms demonstrates how predictive analytics can reveal the secondary risks that are often missed in planning. In Mexico, Hurricane John triggered landslides, overwhelmed emergency responders, and was followed by a spike in violent crime. In contrast, Florida’s coordinated response to Helene enabled recovery within days. These different experiences to very similar events underscores how important it is to blend historical trends with real-time information to predict the impact of a disaster in any given area.
Catching Civil Unrest Before It Begins
One of the most exciting applications of predictive analytics is its ability to anticipate civil unrest. Accurately forecasting social disruptions, such as protests, riots, or large-scale strikes, is essential for maintaining traveler security, but the scale and impact of disruptions can be hard to read. Predictive analytics, however, identifies patterns that signal the potential impact these events will have, helping organizations to better prepare their travelers for what unfolds.
Natural Learning Processing (NLP) plays a key role in the early detection of social unrest. By analyzing text from sources such as social media and local news, NLP gauges public sentiment and can identify rising tensions within communities. Topic modeling highlights recurring issues and the perceived causes of unrest, while Named Entity Recognition (NER) identifies key figures, organizations, and locations linked to protest activity.
When these insights are combined with historical patterns and current events, analysts can judge not only predict the likelihood of unrest occurring, but how disruptive it could be, giving organizations a crucial head start in protecting their travelers and assets. In simple terms, NLP helps analysts tune in to the online mood, pick up rising frustration, anticipate protest locations, and identify key actors.
For example, in June 2025, intelligence providers used NLP and NER to track rising tensions in Nairobi, Kenya. These platforms accurately forecast that unrest and violence were likely to occur in the coming months and, when paired with historical and real-time data, helped to predict high-risk zones, anticipate the government response, and project how major demonstrations might unfold. Multinational organizations used these early insights to reroute travelers, adjust itineraries, and minimize exposure to harm.
Reshaping TRM for a Volatile World
Looking ahead to 2026, business travel is expected to keep growing, but as it does, so will the unpredictably that surrounds it, with travelers navigating political tension, volatile weather patterns and economic pressures on the ground. Risk teams will need to rely less on static or historic assessments and turn more to tools that can read real-time signals and anticipate how situations might shift.
The emphasis will increasingly be on scenario-planning rather than crisis response and travel policies and preparation will need to reflect a broader range of threats that are moving at a faster pace. The organizations that exit 2026 in the best shape will be the ones that treat prediction as a core capability, rather than an add on.
About On Call International
When traveling, every problem is unique–a medical crisis, a political threat, even a missed flight. But every solution starts with customized care that ensures travelers are safe and protected. That’s why for over 30 years, On Call International has provided fully-customized travel risk management and emergency assistance services, protecting millions of travelers, their families, and their organizations. Visit https://www.oncallinternational.com and follow us on LinkedIn to learn more.
