Independent Validation Confirms Scientific Rigor of Greater Than's AI Model for Crash Risk Prediction
-
Greater Than has validated its AI to strengthen trust and transparency in predictive risk intelligence for safe, sustainable mobility - Independent review by AI expert Anders Arpteg confirms scientific rigor of model
- The AI uses real-world driving and crash data to predict crash outcomes regardless of geographies and vehicle types
"Following an independent technical validation, I confirm the scientific rigor of
"Because crash events are statistically rare, effectively training an AI to predict them requires a massive volume of historical and global data," added Anders Arpteg. "Given the model performance, it is clear that sufficient data from many years and countries has been used."
For the validation, the company's Crash Probability Score was used as a representation of the AI model. The review confirmed that drivers with higher Scores were consistently associated with a disproportionate share of claims – regardless of geography, vehicle type or driver category. Both at-fault and not-fault claims were included, reflecting the probabilistic nature of crash risk and avoiding bias related to fault determination.
"Trust and transparency are critical in the world of AI," said Anders Lindelöf, Co-founder & Chief Technology Officer at
At a time when the use of AI is growing rapidly within the mobility landscape,
CONTACT:
Press contact
PR@greaterthan.eu
+46 855 593 200
www.greaterthan.eu
This information was brought to you by Cision http://news.cision.com
The following files are available for download:
|
Independent Validation Confirms Scientific Rigor of |
|
|
https://news.cision.com/greater-than/i/greater-than-validation-press-release-2026-04-08,c3526308 |
|
View original content:https://www.prnewswire.co.uk/news-releases/independent-validation-confirms-scientific-rigor-of-greater-thans-ai-model-for-crash-risk-prediction-302736761.html