StateCover Mutual streamlines case management for 300,000+ documents a year with Affinda’s AI

Fast facts

Business: StateCover Mutual
Industry: Workers compensation
Challenge: Inefficient document classification and data extraction at scale
Solution: Custom AI Document Processing Platform
Results:

• 30–60% increase in processing efficiency
• More than 2x increase in documents sent directly to the right approver
• Reduced costs and time through automated workflows

The challenge

StateCover Mutual is the only specialist provider of workers compensation, safety and wellbeing solutions tailored to New South Wales local government. With a network of 133 Member partnerships, they keep over 39,000people safe, well and working.

But behind the scenes, their team faced a growing challenge: processing more than 300,000 documents annually across over 80 different types, from claim forms and clinical records to medical certificates and return-to-work plans.

Their existing rules-based system wasn’t built for this level of complexity. It required extensive human oversight to review, route, and correct document classification and data extraction errors. Staff had to manually monitor workflows, correct misclassifications, and reprocess documents to ensure they reached the right location.

The team needed a more intelligent solution, one that could:

• Accurately classify a diverse range of document types
• Reliably extract hand written and typed claim numbers
• Identify and extract invoice data
• Minimise human intervention and manual labour
• Support a more scalable, future-ready process

The Affinda Solution

StateCover Mutual partnered with Affinda to design a tailored AI document processing solution. Working closely with their internal team, Affinda deployed three integrated models to address the key pain points:

1. Document Classification Model
   Automatically identifies document type and directs it to the correct location.
2. Claim Number Extraction Model
   Extracts claim numbers accurately – even from handwritten sources – reducing manual checking.
3. Invoice Extraction Model
   Pulls out critical invoice data, even from documents misclassified as invoices, to reduce rework.

Now, when documents are submitted to StateCover, Affinda’s platform:

• Instantly classifies the document by type
• Extracts claim numbers and invoice details
• Auto-validates data or flags it for review
• Routes everything to the appropriate team or system location

This new automated process has fundamentally changed how StateCover handles its document flow.

“StateCover engaged Affinda to develop a tailored AI document processing model. Their support and expertise were invaluable.”

Nick Tran, Business Analyst at StateCover Mutual

The Outcome

With Affinda’s platform in place, StateCover Mutual has achieved measurable, high-impact improvements:

• 60% saving in the time taken to manually review invoices
• 30% drop in manual processing of classification and claim numbers
• More than double the number of insurance documents sent directly to the right approver – without human intervention

As document volumes continue to grow, the new system is reducing manual labour, minimising rework and ensuring faster payment and case handling.

“After facing a few challenges at launch, we now have an accurate and efficient case management system we rely upon. We are in a good position for the system to scale into our future needs. The experience working with Affinda was excellent.”

Nick Tran, Business Analyst at StateCover Mutual