The insurance industry is facing a dual challenge: meeting ever-higher customer expectations while keeping operational costs under control. To answer this challenge and remain competitive, insurers can adopt a strategic combination of Robotic Process Automation (RPA), Artificial Intelligence (AI) and governance structures that together create scalable and intelligent operations. 

A concrete use case illustrates this transformation: the handling of damage history certificates (Schadeverleden Attesten – SVA). These documents, which summarize a customer’s claims history, are essential in managing car insurance policies. Yet until recently, their manual processing was a costly and time-consuming bottleneck. 

 

A Growing Manual Burden 

Each year, European insurers process tens of thousands of damage-history certificates. In Belgium alone, insurers issued 682,917 such certificates in 2023. Against that backdrop, a single large carrier reasonably handles tens of thousands annually; internal operational data at one Belgian insurer shows 40,000+ per year for car insurance alone. Historically, each request triggered a manual workflow: staff opened the incoming email, verified the customer and policy details in core systems, and updated the record before issuing the certificate. 

This repetitive work absorbed around two full-time equivalents (FTEs). Errors were common, caused by manual retyping and mismatches between customer declarations and back-office data. Customer dissatisfaction grew, delays mounted, and the workload risked spiraling further as volumes continued to rise. 

At the same time, the insurer had a broader ambition: to begin adopting Intelligent Document Processing (IDP) as part of its digital transformation roadmap. This meant that the SVA challenge was not just about efficiency, it was a stepping stone toward enterprise-wide automation of document-heavy processes such as claims, underwriting and compliance. 

 

The Business Need: Accuracy, Scalability and Reliability 

The project was framed around three key needs: 

  • Reduce manual burden and free up employees for higher-value tasks. 
  • Improve data quality by eliminating errors introduced through manual re-entry. 
  • Ensure scalability so that increasing volumes would no longer mean increasing headcount. 

Strategically, the SVA initiative was also designed to act as a blueprint for broader IDP adoption, ensuring that future document automation could build on a proven foundation. 

 

The Solution: An Intelligent Three-Step Automation Chain 

The automation journey was structured around a clear three-step model: 

  1. Document recognition & extraction
    Incoming certificates, captured via a central mailbox, were fed into an AI engine combining OCR and intelligent classification. The system recognized the SVA document type and extracted key data points such as customer name, policy number and claims history. 
  2. Validation & cross-checking
    RPA bots compared the extracted data against the core policy administration system. Business rules ensured that mandatory fields were completed and verified that extracted values matched existing contract information. Validated certificates were automatically updated in the system. 
  3. Exception handling & follow-up
    When data was missing or inconsistent, the system triggered alerts or back-office tasks. Many issues could be resolved automatically; only complex cases required human intervention. 

The target process was captured in a redesigned TO-BE model, ensuring alignment between IT, operations and management. 

 

Results Achieved 

The pilot delivered immediate, measurable benefits: 

  • 2 FTEs saved annually by automating document extraction and validation. 
  • A further 4 FTE savings potential identified by extending automation to follow-ups and reminders. 
  • Fewer errors, thanks to eliminating manual retyping. 
  • Improved customer experience, with faster turnaround times and more reliable data. 

Beyond numbers, the project became a reference case for IDP in the organization, serving as a model to be replicated across other document-heavy domains such as claims processing, underwriting, and compliance. 

 

From Automation to a Scalable Operating Model 

This use case moves beyond task automation by applying five delivery principles that make results durable at scale: 

  • Process first: simplify the SVA “process” journey (intake → verify → issue) before making it faster. 
  • Right tech, right place: use RPA for repeatable steps, rules for certainty and AI only where ambiguity exists (OCR/classification/validation). 
  • Connected flow: integrate with portals, core systems and partner endpoints so data travels once via APIs and events, not copy-paste. 
  • Controls by design: embed audit trails, ownership and privacy/security checks into the flow, not after it. 
  • Learn in the loop: track cycle time, first-time-right and exception rates; feed insights into the next improvement sprint. 

Applied to damage-history certificates, these principles turn a manual inbox into an event-driven, auditable service with clear ownership and measurable outcomes shorter turnaround, fewer errors and sustained FTE relief. 

 

A Broader Industry Trend 

The success of this initiative is not an isolated case, it reflects structural changes across the insurance industry. 

According to McKinsey, up to 45% of tasks in the insurance sector can already be automated with existing technology, particularly administrative and operational tasks. Furthermore, insurers that integrate AI more deeply generate on average six times more value for shareholders than less advanced peers. 

Meanwhile, Gartner highlights Intelligent Document Processing (IDP) as a cornerstone of hyperautomation in insurance. The firm projects the global IDP market will exceed $2 billion by 2026, driven by adoption in document-intensive processes such as claims management and underwriting (Gartner). 

These findings reinforce the idea that automating damage history certificates is part of a much broader industry shift, one that will fundamentally reshape how insurers operate. 

 

Future Outlook 

The damage-history certificate project is only the first step. Its success creates a blueprint for broader impact across the value chain: 

  • Scale-out to adjacent processes. Replicate the pattern in claims handling, underwriting workflows, policy servicing, billing and compliance, using a reusable catalog (connectors, decision tables, OCR/ML components, prompts) so new use cases onboard in weeks, not months. 
  • API-first, event-driven integration. Expose secure, versioned APIs and webhooks to connect brokers, customers, and partners; apply idempotency and asynchronous events to enable straight-through processing and multi-channel experiences. 
  • Governance that accelerates replication. A dedicated Hyperautomation CoE enforces design and control standards (privacy, security, model risk) and runs gated promotion (pilot → product → enterprise), portfolio intake, and reuse KPIs ensuring scale and compliance by design. 
  • Analytics & GenAI in-flow. Use process mining to target bottlenecks; embed GenAI for guided intake, exception triage, and correspondence generation with retrieval-augmented grounding and human-in-the-loop controls. 

As GenAI converges with RPA and classical AI/decisioning, insurers can graduate from isolated pilots to enterprise platforms, measured by FTE-relief, cycle-time reduction, right-first-time rates and NPS. The result is not just incremental savings, but a durable reinvention of the operating model, with controlled risk, faster time-to-value and a pipeline that continually extends HyperAutomation to the next highest-ROI process. 

 

Conclusion 

Automating damage-history certificates shows how HyperAutomation turns a routine task into a strategic lever for efficiency, quality and scale. By combining software robots (RPA), AI and clear governance, the insurer saved 2 FTE immediately, with +4 FTE potential identified. 

More importantly, the team established a scalable pattern reusable system connectors and rule tables, API-first integrations and controls built in so new work can ride the same rails. 

This isn’t limited to certificates. Thanks to reusable connectors, shared rule tables, standard templates and built-in controls any new document flow (underwriting letters, policy changes, compliance notices) can be launched within weeks, not months. The blueprint accelerates IDP across underwriting, claims, and compliance while improving turnaround time, first-time-right and auditability. 

McKinsey’s and Gartner’s analyses confirm that these results are part of a larger movement reshaping the insurance sector. As HyperAutomation becomes mainstream, insurers will no longer compete simply on products and pricing but on their ability to orchestrate intelligent, scalable operations that deliver sustained customer value.