Case Study
AI claims automation case study covering motor claims, damage detection, fraud reduction, image recognition, deep learning, and faster insurance assessment.
AI Claims Automation helped a general insurance company enable automated vehicle claims and damage assessment in under 180 seconds.
The project focused on replacing slow, manual, error-prone inspection processes with technology-enabled damage detection, classification, estimation, and fraud prevention.
Traditional auto insurance claims were heavily manual, costly, slow, and prone to errors. Manual vehicle inspection took time, customer experience suffered, legacy rules and siloed systems increased inefficiency, and the claims process did not learn effectively from historical data.
The client needed a simplified vehicle claims assessment process, stronger customer trust, fraud and error reduction, image and video based real-time claim price estimation, technology-enabled damage detection and classification, accurate pricing, and automated invoice reading and mapping.
Before automation, more than 70% of the process was manual, turnaround time ranged from two to seven days, data-driven insight was limited, inspection depended on surveyors, the process was error-prone, and the customer experience suffered.
MAQ simplified claims assessment with image recognition, deep learning, and neural networks. The solution identifies vehicle parts, detects damage, classifies repair, replacement, and painting requirements by severity, estimates prices based on vendor, location, state, and damaged parts, and applies fraud detection through image misuse checks and make-and-model verification.
The solution enabled real-time vehicle damage detection, easier damage calculation, historical data based matching, reduced manual intervention, real-time verification, digital document handling, AI/ML guided video and image capture, 360-degree video analysis, and a no-touch self-claims assessment flow.
The case study reported up to 300% improvement in claims processing time, turnaround reduction up to 75%, fraud image detection reduction by 40%, no-touch process improvement by 70%, up to 50% increase in customer happiness, reduced per-claim cost up to 40%, and stronger predictive decision-making.
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