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Solera Auto-Authorizes 50% of Claims

DoiT guided the Google Cloud AI/ML architecture behind Qapter, enabling touchless processing of 300 million transactions a year.

Cloud Intelligence™
Solera Holdings

Meet Solera

Solera Holdings is the market leader in automobile damage estimation, transforming how insurance and automotive professionals serve customers since 2005. The company manages 300 million transactions between insurance companies, drivers, and the automotive industry annually, representing 60% of all claims worldwide. With an extensive database of proprietary damage claims images and payments, Solera launched Qapter in 2020 using Google Cloud AI/ML products to power an end-to-end claims management system.

The Challenge

Solera needed to automate their automobile collision claim workflow using AI and machine learning. Insurance companies had struggled to scale computer vision solutions for damage assessment, often failing to move beyond research projects to productized solutions. The team required sophisticated AI technologies integrated with cutting-edge cloud capabilities.

The Solution

Solera chose Google Cloud's AI/ML solutions after a thorough technology evaluation. They leveraged Cloud Vision for image processing, TensorFlow for custom algorithms, Cloud GPUs and TPUs for accelerated processing, and Google Kubernetes Engine for orchestration. DoiT International provided expertise on platform architecture and implementation best practices.

Results

  • Auto-authorizes 50% of damage claims, reducing estimating costs by nearly half
  • Processes 300 million transactions annually representing 60% of global claims
  • Eliminates need for physical claims adjuster visits through AI-powered damage assessment
  • Provides contactless claims processing during COVID-19 pandemic

DoiT demonstrated deep knowledge of both Google Cloud and the full-stack infrastructure that Qapter requires.

Evan Davies, Chief Technology Officer, Solera Holdings

The Challenge: Matching Technology to the Problem

When CTO Evan Davies joined Solera in 2020, he knew multiple technologies would be required to solve complex business problems. Insurance companies had struggled to commercialize computer vision solutions for collision damage repair, often building working solutions but failing to scale. Solera needed to apply mature AI technologies effectively while maintaining their proven backend systems. The team required sophisticated cloud and AI technologies to reimagine Qapter and streamline the estimation process globally.

Choosing Google Cloud for AI Innovation

After a thorough technology evaluation, Google Cloud's AI/ML solutions proved more sophisticated, robust, and scalable than competitors. The tight integration between AI technologies and the entire Google Cloud portfolio was decisive. Solera leverages Cloud Vision for image processing and OCR, TensorFlow for custom algorithms, and Cloud GPUs and TPUs for accelerated processing. This single-source approach provides faster processing speed and sophisticated tools that complement development focus.

The Solution: Innovation with Expert Support

Solera partnered with Google Cloud engineers and DoiT International for platform architecture advice and implementation best practices. The new Qapter version launched in 2020 dramatically transforms customer offerings. When drivers upload damage pictures, the platform leverages its huge repository to estimate damage extent, recognize vehicle make and model, identify required parts, and calculate repair costs without physical claims adjuster visits.

Results: Self-Service Claims Processing

Customers in France and The Netherlands are thrilled with Qapter's AI-powered damage claims workflow. The system now auto-authorizes 50% of damage claims, reducing estimating costs by nearly half. During COVID-19, the platform provides unexpected benefits by reducing human interactions while maintaining necessary services. The three-dimensional vehicle models can be repurposed for various workflows, creating a solid basis for future product development.

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