Automating Address Validation and Reduced Processing Time by 90% for a Leading Specialty Insurance Provider

Industry: Insurance | Services: Automation

Client Overview

The client is a leading American company specializing in insurance and lifestyle services for classic and collector vehicles. As the largest global provider of specialty insurance for classic cars, they deliver innovative coverage solutions through their extensive network of brokers and agencies, helping clients protect their valuable vehicles with reliability and confidence.

The Challenge

Delayed Operations and Increased Compliance Risks in Manual Address Change Processing

The client’s address change request process (VLAD) was entirely manual, requiring service agents to validate and update each case individually. Handling nearly 14,000 to 15,000 requests every month meant dedicating 3.2 full-time employees solely to this repetitive task, with each taking 8 to 10 minutes per verification.

This labor-intensive approach strained SLA commitments tied to a three-day turnaround. Frequent data entry errors and inconsistent handling across address scenarios, such as PO box updates, state changes, or unchanged addresses, also introduced compliance risks. As volumes continued to rise, the process became increasingly unsustainable, limiting scalability and driving operational costs higher.

The client needed an automated, high-accuracy solution that could process address changes at scale without adding headcount or compromising control.

Sutherland Solution

End-to-End Automation of the VLAD Request Workflow

To resolve the operational strain caused by manual processing, Sutherland’s Intelligent Automation team began a deep-dive assessment of the client’s VLAD workflow. This involved understanding how requests were retrieved from the core processing system (Drive Train), how agents interpreted address variations across scenarios, and how updates were executed across Drive Train and Salesforce.

Key bottlenecks, including repetitive comparisons, inconsistent scenario handling, and a lack of audit visibility, were mapped to establish the automation logic.

1. Workflow Mapping and Scenario Definition
   The Intelligent Automation team first categorized all address change requests into four scenarios: Same State, PO Address, Same Address, and Different State, mirroring how human agents processed them. This classification formed the foundation of decision rules for the bot to determine whether the update should be applied directly in Drive Train or executed within Salesforce before syncing back to Drive Train.
2. Automated Request Retrieval and Validation
   Bots were configured to log into Drive Train, retrieve VLAD files from the Support Team queue, and compare the new address field against the existing record. Based on predefined rules, they identified whether updates could be auto applied or required deeper verification.
3. Automated Address Updates Across Platforms
   For Same State and Same Address requests, updates were executed directly in Drive Train using the appropriate endorsement selection and ZIP correction steps. For PO or out-of-state changes, the bot accessed Salesforce, navigated to the customer profile, and updated the address before syncing the changes back to Drive Train.
4. Built-in Exception Handling and Traceability
   If any request contained incomplete data or mismatched customer references, it was automatically flagged and reassigned with comments for manual intervention. Every bot action—from update execution to handoff—was logged with timestamped notes to ensure audit readiness and full traceability.
5. Ensuring SLA Continuity and Operational Stability
   With automated processing operating around the clock, the VLAD workflow now consistently meets turnaround commitments without backlog accumulation. Exceptions are clearly routed, while standard cases are completed in under a minute, ensuring uninterrupted service delivery.

Through this structured automation rollout, Sutherland converted the client’s labor-dependent address change process into a scalable, rule-driven operation, reducing effort, strengthening compliance controls, and laying the foundation for end-to-end service automation.

The Outcome

Faster Processing, Higher Accuracy, and Consistent SLA Compliance

The automation of the client’s address change process delivered measurable improvements in both speed and consistency.

Tasks that previously required 3.2 full-time employees and 8–10 minutes per request are now completed in under 1 minute across all bot-managed cases. Email-based exceptions, previously handled manually across 400–500 instances per month, have also been automated, further reducing agent intervention.

SLA performance now consistently meets the two-day turnaround target, and accuracy in endorsement handling has greatly improved with standardized rule execution. With repetitive verification work removed from daily queues, service agents were redeployed to higher-value customer interactions and exception management.

This transformation not only accelerated throughput and strengthened compliance control but also solidified the client’s trust in Sutherland as a long-term automation partner focused on scalable operational excellence.