Automated case management in cloud reduces customer claims resolution time

BPM OutSystems
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1

We implemented OutSystems Case Management in our cloud environment to automate customer claims processing for our insurance division. The previous manual workflow involved 7-12 days average resolution time with multiple handoffs between departments.

Our implementation focused on three key areas: automated case routing based on claim type and complexity, REST API integration with our policy management system for real-time data validation, and comprehensive SLA monitoring with escalation alerts.

The results after six months in production have been remarkable. We’ve reduced average claims resolution from 9.5 days to 2.8 days, eliminated 73% of manual data entry tasks, and improved customer satisfaction scores by 42%. The cloud deployment gave us the flexibility to scale during peak claim periods without infrastructure concerns.

I wanted to share our implementation approach and lessons learned for anyone considering automated case management in cloud environments.

2

We used a hybrid approach. OutSystems’ native routing handles straightforward cases (claim amount, type, region), but we built custom routing logic for complex scenarios. The system evaluates claim complexity using a scoring algorithm that considers claim amount, policy type, customer history, and fraud indicators. This score determines whether cases go to junior adjusters, senior adjusters, or specialized teams. Adjuster availability is tracked in real-time through integration with our workforce management system.

3

What challenges did you face with the REST API integration to your policy management system? We’re planning something similar and concerned about latency and data synchronization issues, especially with the cloud deployment adding network hops.

4

This is impressive! Can you share more details about how you configured the automated case routing? We’re struggling with routing logic that accounts for both claim complexity and adjuster availability. Did you use OutSystems’ built-in routing rules or custom logic?

5

Your SLA monitoring implementation sounds robust. How granular did you make the SLA tracking? Are you monitoring at the case level, individual task level, or both? And how do you handle SLA adjustments when cases get escalated or reassigned?

6

The REST API integration was actually one of the smoother parts of the implementation. We use asynchronous API calls for non-critical data retrieval to avoid blocking case creation. Critical validations (policy status, coverage verification) are synchronous but cached aggressively - we refresh cache every 15 minutes for active policies. Cloud latency averaged 45-80ms, which was acceptable. The bigger challenge was handling API failures gracefully - we implemented circuit breaker patterns and fallback mechanisms to prevent case processing from stalling.

7

I’m curious about the scalability aspect you mentioned. During peak claim periods (say after a major weather event), how does the cloud deployment handle the load spike? Did you implement auto-scaling, and if so, what metrics trigger scaling events? Also, how do you ensure consistent case processing performance during these peaks?

8

Let me provide a comprehensive overview of our implementation covering all three focus areas:

Automated Case Routing Architecture:

Our routing system operates on a three-tier classification model. First, incoming claims are scored on a complexity scale (1-10) using a weighted algorithm that evaluates:

  • Claim amount (30% weight): <$5K=2pts, $5K-$25K=5pts, >$25K=8pts
  • Policy complexity (25% weight): Standard=2pts, Premium=5pts, Commercial=9pts
  • Customer history (20% weight): Claims history, tenure, dispute records
  • Fraud indicators (15% weight): Pattern matching against known fraud signatures
  • Documentation completeness (10% weight): Missing docs add complexity points

Based on the complexity score:

  • Score 1-3: Auto-assigned to junior adjusters via round-robin
  • Score 4-7: Skill-based routing to available senior adjusters
  • Score 8-10: Assigned to specialized teams (fraud, commercial, legal)

The system checks adjuster availability in real-time through workforce management API integration. If all qualified adjusters are at capacity, cases enter a priority queue with escalation alerts triggered at 2-hour intervals. We also implemented geographic routing preferences - local adjusters are preferred for claims requiring site visits.

Key technical implementation: We built a custom “RoutingEngine” server action that executes on case creation. It queries adjuster availability, evaluates routing rules, and assigns cases within 2-3 seconds average. The routing logic is configuration-driven, allowing business users to modify routing rules without code changes through a dedicated admin interface.

REST API Integration Implementation:

Our policy management system integration uses a microservices architecture with three primary API endpoints:

  1. Policy Validation Endpoint (synchronous, 200ms SLA):

    • Verifies policy active status and coverage details
    • Called during case creation with 3-second timeout
    • Implements circuit breaker pattern (trips after 3 consecutive failures)
    • Fallback: Manual validation workflow if API unavailable

  2. Customer History Endpoint (asynchronous, cached):

    • Retrieves claim history, payment records, policy changes
    • Called after case creation, results populate case context within 5 seconds
    • Cached for 15 minutes with Redis implementation
    • Reduces API calls by 85% during high-volume periods

  3. Document Retrieval Endpoint (on-demand):

    • Fetches policy documents, prior claim files, correspondence
    • Called only when adjuster requests specific documents
    • Implements progressive loading for large document sets

The cloud deployment advantage: We leveraged Azure API Management for rate limiting, authentication, and monitoring. This abstraction layer protects our policy system from overload while providing detailed analytics on API performance. Average API latency in cloud: 45-80ms (well within acceptable thresholds). We implemented retry logic with exponential backoff for transient failures.

Data synchronization strategy: Rather than real-time sync, we use event-driven updates. When policy changes occur in the source system, events are published to Azure Service Bus, which triggers case data refresh in OutSystems. This approach reduced synchronization overhead by 70% compared to polling.

SLA Monitoring and Escalation Framework:

We implemented multi-level SLA tracking with distinct timelines for different case types:

  • Simple claims (complexity 1-3): 48-hour resolution SLA
  • Standard claims (complexity 4-7): 96-hour resolution SLA
  • Complex claims (complexity 8-10): 168-hour resolution SLA

SLA tracking operates at both case and task levels:

Case-Level SLA:

  • Timer starts at case creation
  • Pauses during customer information requests (waiting on external input)
  • Resumes when customer responds
  • Escalates at 75% of SLA elapsed (yellow alert) and 90% (red alert)
  • Escalation routing: Yellow→Team Lead, Red→Department Manager

Task-Level SLA:

  • Each case workflow contains 5-8 tasks (initial review, document verification, assessment, approval, notification)
  • Individual tasks have micro-SLAs (typically 4-24 hours depending on task type)
  • Task SLA breaches trigger reassignment to available adjusters
  • Three consecutive task breaches trigger case escalation regardless of overall case SLA

SLA adjustment logic: When cases are escalated or reassigned, the SLA clock doesn’t reset, but we add “complexity buffers”:

  • First escalation: +12 hours added to remaining SLA
  • Reassignment due to adjuster unavailability: +6 hours
  • Customer-requested escalation: No time adjustment (expedited processing)

The SLA monitoring dashboard provides real-time visibility:

  • Cases at risk (within 20% of SLA breach): 23 currently
  • Cases in breach: 4 currently (0.8% of active cases)
  • Average SLA utilization: 62% (healthy buffer)
  • Adjuster performance metrics: Cases resolved within SLA by adjuster

Cloud Scalability and Performance:

Our Azure cloud deployment uses auto-scaling based on multiple metrics:

Scaling Triggers:

  • CPU utilization >70% for 5 minutes: Scale out +1 instance
  • Active case queue depth >200: Scale out +1 instance
  • API request rate >500/minute: Scale out +1 instance
  • Memory utilization >80%: Vertical scaling (increase instance size)

Scaling Limits:

  • Minimum instances: 2 (high availability)
  • Maximum instances: 8 (cost control)
  • Scale-out time: 3-4 minutes (Azure Container Instances)
  • Scale-in delay: 15 minutes (prevent thrashing)

During peak periods (e.g., Hurricane season, major weather events), we’ve observed:

  • Normal load: 2-3 instances handling 150-300 cases/day
  • Peak load: 6-8 instances handling 800-1200 cases/day
  • Performance degradation: <5% during scaling events
  • Case processing time variance: 2.8 days (normal) vs 3.1 days (peak)

Performance consistency measures:

  • Database connection pooling (50 connections per instance)
  • Asynchronous processing for non-critical operations
  • Queue-based case distribution (prevents instance overload)
  • Health check endpoints (remove unhealthy instances from load balancer)

Business Impact Summary:

  • Resolution Time: 9.5 days → 2.8 days (71% reduction)
  • Manual Data Entry: 73% elimination (automated policy data retrieval)
  • Customer Satisfaction: +42 percentage points (from 58% to 85% satisfied)
  • Adjuster Productivity: +38% (reduced administrative overhead)
  • SLA Compliance: 94.2% (target was 90%)
  • Cost Reduction: 31% lower operational costs despite 22% case volume increase
  • Fraud Detection: 18% improvement in early fraud identification

Key Success Factors:

  1. Executive sponsorship ensured resources and priority
  2. Phased rollout (pilot with 20% of cases, gradual expansion over 3 months)
  3. Extensive adjuster training (40 hours per user)
  4. Change management focus (addressed resistance to automation)
  5. Continuous monitoring and optimization (bi-weekly performance reviews)

Lessons Learned:

  • Start with simpler case types to build confidence
  • Invest heavily in data quality before automation (garbage in, garbage out)
  • Don’t over-automate initially - keep human oversight for complex cases
  • Cloud costs can escalate - implement cost monitoring and optimization from day one
  • API integration resilience is critical - plan for failures
  • SLA definitions require business stakeholder alignment - technical implementation is the easy part

The combination of intelligent routing, robust API integration, and comprehensive SLA monitoring created a transformative impact on our claims operation. The cloud deployment was essential for handling variable workloads and provided operational flexibility we couldn’t achieve on-premise.