- Challenge: High-volume document classification and naming was manually intensive and creating significant administrative rework across Suncorp’s workers compensation operations.
- Solution: Content AI uses AI reasoning to support document classification and automated naming at scale.
- Business impact: Now live in production, the solution is projected to save 68,000 hours of manual effort each year.
Business challenge
Suncorp processes more than 1.1 million workers compensation claim documents each year across two major document categories alone. Every document needs to be categorised and given a short name so it can be appropriately actioned.
Historically, classification and naming relied heavily on human comprehension, interpretation and validation against a complex matrix of business rules and reference samples. Documents can appear similar while requiring different handling, making consistent classification difficult at scale.
Solution
Classification and naming require more than fixed rules. Content AI uses carefully engineered AI reasoning to understand the content and context of claim documents and support consistent processing at scale.
Working with our team, Suncorp implemented the solution through a phased approach. Within three months, the teams had delivered a working solution covering 62% of total document volume across Suncorp’s two largest claim document categories.
In the first phase, Content AI was used for document classification and automated document naming. The outputs give Suncorp’s claims specialists more consistent information to work from, helping them progress claims more efficiently while retaining responsibility for the final assessment.
How it works
Content AI analyses document content and contextual signals to support consistent classification across large document volumes.
Automated naming reduces reliance on repetitive manual interpretation against complex business rules and reference samples.
Operational reporting is combined with Suncorp’s internal analytics to monitor performance, identify edge cases and guide ongoing improvement.
Building confidence before scaling
The phased approach helped manage risk and build confidence in Content AI outputs. Realistic test datasets including redacted production documents were used to compare existing classifications with AI-assisted results before production deployment.
Change management was treated as part of the implementation. Feedback from claims teams helped identify where the technology required refinement and where processes or ways of working could also improve.
Results & outcomes
The solution is live in production and delivering measurable impact across Suncorp’s workers compensation operations.
Be data-led. Continuously measure outcomes. The ability to combine Fujifilm’s operational reporting with our internal data has allowed us to validate results, identify new opportunities and continually improve performance.
Suncorp
Collaboration and continuous improvement
Suncorp recognises that AI is not a one-off implementation. The teams continue to review solution performance, user feedback and edge cases to improve both the technology and the surrounding business process.
Combining operational reporting with Suncorp’s internal analytics helps determine whether improvement opportunities sit within the technology itself or in change management and the way teams work.
About Suncorp
Suncorp is an ASX-listed insurance company headquartered in Brisbane, Australia, with Australian portfolios spanning consumer insurance, commercial insurance and personal injury insurance, including workers compensation.
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