
AI for proactive risk management in fleet insurance
Using artificial intelligence, motor insurers can make informed business decisions based on actual risk and proactively influence customer risk with data-driven insights.
Using artificial intelligence, motor insurers can make informed business decisions based on actual risk and proactively influence customer risk with data-driven insights.
Greater Than’s Crash Probability Dashboard provides a dynamic view of risk level in real-time across an entire driving population.
AI in risk management can predict crashes before they happen, putting risk managers in greater control than ever before. AI can also predict climate impact, supporting organizations with sustainability and ESG.
With so many risk management solutions available, managing safety and sustainability can feel overwhelming for fleets. With AI, the management process is simplified.
The theme of the 2023 World Day for Safety and Health at Work is “a safe and healthy working environment is a fundamental principle and right at work.” We've chosen to examine the way fleet risk management fits into this.
We’re pleased to support efforts to reduce road crashes and have chosen to join the conversation around Distracted Driving Awareness Month by taking a wider view of distracted driving.
AI pattern profiling in driver risk management works in a similar way to facial recognition, but instead of comparing data to faces, it compares it to a database of known vehicle trips.
Pattern AI in motor insurance is facilitating the prediction of not only crash probability, but also crash type and associated cost, enabling accurate loss forecasting.
The needs of fleets are changing fast. Thankfully, AI is enabling driver risk management providers to take their solutions to another level by identifying crash probability and climate impact.
Sustainability regulations are changing fast, and the current lack of uncertainty is a challenge for organizations.
Managing safety is a number one priority for many fleets and mobility companies. The challenge is how to measure and understand driver health while respecting driver privacy.
With artificial intelligence, any fleet with GPS data can rapidly access insights to evaluate how driver behavior corresponds with safety and environmental impact.