Trygg-Hansa

In the property and casualty insurance sector, speed of claims resolution directly drives policyholder retention and brand trust. Trygg-Hansa's personal property claims process — covering high-dependency devices such as mobile phones, tablets, and laptops — was failing on both fronts. A newly formed team assigned to handle home insurance claims lacked established workflows, leading to inconsistent processing times and delayed reimbursements for customers who depended on those devices daily. Back-office operations ran on paper-based processes riddled with non-value-added manual steps, creating a bottleneck that frustrated both staff and claimants. Without real-time claim visibility, inbound customer service calls mounted, consuming agent capacity without advancing resolution.

Trygg-Hansa partnered with SS&C Blue Prism to deploy digital workers — internally branded as 'Steve' — that combined robotic process automation with an analytics-driven machine learning algorithm purpose-built for fraud risk scoring and claims routing. When a personal property claim is submitted, the ML model evaluates risk signals and assigns a fraud-risk classification. Low-risk, qualifying claims are immediately fast-tracked: the digital worker autonomously assesses the claim, applies payment processing logic, updates the policy record, and sends a resolution notification to the customer through the self-service portal — all without human intervention. Higher-risk or complex claims are escalated to human adjusters with enriched data already compiled, reducing their handling time. The integration connected directly to Trygg-Hansa's existing back-office and customer portal systems, eliminating the paper-based handoffs that had previously created delays.

The automation program delivered measurable improvements across speed, satisfaction, and operational efficiency:

• 95% reduction in processing time for fast-tracked personal property claims, enabling near-immediate reimbursements for qualifying customers
• 7% increase in customer satisfaction (CSAT) scores, reflecting the direct link between claims speed and policyholder experience
• 35% decrease in non-value-added inbound calls, as customers could track status in real time through the portal rather than calling for updates

Beyond throughput gains, the digital workers surfaced a material recovery opportunity: by cross-referencing claim data, the system identified claims that should have been settled by a different insurer, recovering millions of euros that would otherwise have gone undetected. Human adjusters shifted from routine data entry to higher-complexity case work.

• Combining RPA with an ML fraud-scoring layer enables true straight-through processing — neither technology alone can deliver end-to-end automation for regulated claims workflows.
• Defining a clear 'fast-track' eligibility threshold is foundational; the quality of that routing logic determines both automation rates and fraud exposure.
• Framing the program around customer experience rather than cost reduction produced CSAT gains that justified the investment beyond operational savings.
• Digital workers can surface secondary value — such as subrogation or inter-insurer recovery — when given access to cross-referencing logic at the point of processing.
• Change management for the claims team matters: agents need clear visibility into what the digital worker handles versus what requires human judgment to build trust in the system.