IAG

Motor vehicle insurance claims, particularly total loss assessments, required vehicles to be towed to a garage for physical inspection, making the process slow and emotionally taxing for customers. Claims could take more than three weeks to resolve, leaving customers without clarity during a stressful time.

IAG developed an in-house predictive total loss solution using machine learning that analyzes information provided by customers during the claims process — either by phone or online. The system predicts with over 90% accuracy whether a vehicle is a total loss, eliminating the need for a physical garage assessment in many cases. Customers are notified via text message the following day with the outcome and guidance on next steps.

Total loss claim processing times were reduced from more than three weeks to just three days. Customer advocacy scores, as measured through total loss customer experience surveys, showed a significant uplift. The platform was also among the first in Australia evaluated against the government's voluntary AI ethics principles.

• In-house development of predictive ML can dramatically compress claims cycle times without requiring third-party vendors.
• Proactive, transparent customer communication (e.g., same-day text notification) is as important as the efficiency gain itself for improving customer satisfaction.
• Aligning AI deployments with emerging ethics frameworks early builds trust and positions the insurer ahead of likely future regulatory requirements.