Shriram Finance
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Process automation
icare · SAS
Independently reportedInsuranceAustraliaScaledPublic sectorClassical MLLive 2019
Independently reported. An independent or peer-reviewed source reports this result, alongside the vendor.
Reported by govtechreview.com.au (independent reporting). Checked against the source page on 2026-09-05. 2 of 2 figures below appear on that page word for word.
The problem
Workers' compensation claims were processed using a standard, one-size-fits-all approach for 30 years, not matching individual needs.
What was deployed
- Vendor
- SAS
- Products
- SAS Enterprise Miner
- Technique
- Classical ML
- Build or buy
- Bought and customised
- Deployment
- On-premise
- Data used
- structured data including 96 different data points and 61 biopsychosocial variables per claim; over a year’s worth of de-identified claims data
- Scale
- 10 triage requests per second during peak time
- Timeline
- 6 months
What changed
Each row is quoted from the source. Figures we could not find on the page in those words are marked — they are kept, not deleted, so you can judge them.
increased by 15%accuracy
“Using structured data including 96 different data points and 61 biopsychosocial variables per claim, icare shifted from a regression model to a random forest model from SAS Enterprise Miner, increasing accuracy by 15%.”
Quoted word for word from the source · round percentage, no baseline given
10 per secondtriage requests processedduring peak time
“icare are now able to respond to 10 triage requests per second during peak time, which ensures the team can service the needs of customers as quickly and efficiently as possible”
Quoted word for word from the source
“This level of performance is critical as icare are now able to respond to 10 triage requests per second during peak time, which ensures the team can service the needs of customers as quickly and efficiently as possible”
Difficulties and limits
- aligning expectations upfront and planning integration with front-end systems
- preparing data for both modelling and effective triage
Sources
- govtechreview.com.au — independent reporting
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