09/17/2025 | News release | Distributed by Public on 09/16/2025 23:44
Within risk management, traditional indicators of risk include harsh acceleration and braking, speeding, and mobile phone use. These indicators are typically triggered when a driver passes a certain threshold, suggesting that the driver has a high crash risk due to these behaviors. But what happens between these triggered events?
At Greater Than, we look at risk differently. Our AI analyzes the whole trip, identifying the full spectrum of driving behaviors to uncover patterns in driving behavior that we then cross reference with our database of over 7 billion real-life driving patterns, to predict trip outcomes. So, what exactly are driving patterns?
Think about facial recognition. This is a system of technology that's capable of matching a human face against a database of billions of other real faces. In AI, driving patterns are similar. But instead of comparing faces, our AI compares the way people drive against actual trip outcomes, identifying similarities and predicting crash probability.
Our database has been trained with real trips from 106 countries and 1,600 cities, including both insurance claims and no-claim trips. Because of this extensive level of training, our AI is not only able to predict the likelihood of a crash occurring, but also what type of crash.
While traditional telematics systems might meet many needs, they do have pitfalls when it comes to the understanding of risk and crash prediction. Driver scores that are only based on triggered events don't provide a comprehensive overview of risk; they provide more of a snapshot of isolated events throughout each trip.
AI fills those gaps. And our research shows that this is important. In terms of behaviors that contribute to high risk, these can build before events are triggered, and early identification enables faster risk mitigation. Equally important is identifying safe behaviors that often go undetected by traditional systems. Knowing which of your drivers are safest, and why, is essential for positive reinforcement to strengthen your overall safety culture.
AI is complex, and its capabilities are so extensive that they can be difficult to understand. Many people today use ChatGPT for many uses, so are familiar with the speed at which it is able to complete a wide variety of requests.
Using AI in risk management is not all that different. It is fed with driving data from any source - usually obtained via an API connection - and it rapidly analyzes the data to identify patterns and predict outcomes. The higher the frequency of the data (every second, for example), the more detailed and accurate the predictions are. And these predictions can be seamlessly integrated into existing solutions, optimizing risk management efforts while strengthening safety programs.
Utilizing technology that analyzes driving patterns doesn't mean replacing your current tools; it means enhancing them with deeper insights. By tapping into the power of AI-driven pattern recognition, you gain access to a previously invisible layer of driver behavior; the full context behind every trip, not just isolated incidents.
Incorporating driving pattern intelligence into your risk management strategy equips you with a full spectrum overview of driver behavior and risk. And, with seamless integration into your existing tools, it's easy to amplify the value of your telematics or safety solutions.
To learn more about driving patterns and how they can enhance your driver safety programs, please book a demo.