Dynamic Scaling and Task Management

Building an enterprise-level AI module for travel insurance claims is complex. Claims processing requires handling diverse data formats, interpreting detailed information, and applying judgment beyond simple automation.

When developing Lea’s AI claims module, we faced challenges like outdated legacy systems, inconsistent data formats, and evolving fraud tactics. These hurdles demanded not only technical skill but also adaptability and problem-solving.

In this article series, we’ll share the in-depth journey of building Lea’s AI eligibility assessment module: the challenges, key insights, and technical solutions we applied to create an enterprise-ready system for travel insurance claims processing.

Challenge : Dynamic Scaling and Task Management

Key Learnings :

Dynamic Scaling for Demand Surges: Ancileo’s cloud-based system uses auto-scaling and container orchestration to handle claims spikes due to unexpected events, ensuring uninterrupted processing and efficient resource use.
Asynchronous Task Management for Efficiency: By prioritizing and distributing tasks with Celery and Redis, the system prevents overload, maintaining steady and reliable claims processing even during peak times.
Centralized Coordination for Workflow Control: A task manager coordinates multi-stage claims processing by dynamically allocating resources and managing dependencies, reducing processing time and improving service quality.

In travel insurance, demand surges often stem from large-scale disruptions, like natural disasters or regional crises, requiring systems that can scale quickly and manage complex workflows. Ancileo’s approach leverages cloud-based infrastructure, dynamic scaling, and asynchronous processing to handle high volumes while maintaining performance. Here’s how each capability meets the demands of travel insurance claims processing:

Implementing Dynamic Scaling with Cloud Resources

To manage fluctuating demand, Ancileo uses a cloud-based dynamic scaling approach, ensuring that during demand surges—such as post-flight delays or natural disasters—the system can adjust resources to keep up with claim volume without delays.

Kubernetes for Container Orchestration: Kubernetes manages containerized applications, automatically adjusting the number of containers based on real-time demand. During surges, Kubernetes deploys more containers, maintaining uninterrupted processing and decommissioning them during low demand to reduce costs.
Auto-Scaling Policies: Pre-defined auto-scaling policies on Kubernetes or cloud platforms monitor performance (e.g., CPU and memory usage) and trigger scaling events when certain thresholds are met. This real-time responsiveness ensures that resources always match demand, optimizing performance without wasted resources.

By scaling resources as needed, travel insurers reduce operational costs while ensuring reliable processing speeds. This flexibility helps them respond quickly, even during high-traffic periods, minimizing customer wait times and improving the claims experience.

Asynchronous Task Management with Celery and Redis

Ancileo’s use of Celery and Redis enables efficient claims processing by queueing, prioritizing, and processing tasks independently. This setup reduces system overload and maintains smooth operation, even during demand surges.

Task Queueing and Distribution: Redis acts as a message broker, queuing incoming claims as they enter the system. Each claim is picked up by an available Celery worker for asynchronous processing, preventing delays and allowing multiple claims to be handled simultaneously—a valuable setup during events like regional flight delays.
Task Prioritization: High-priority claims, such as emergency medical cases, are prioritized in the queue to ensure immediate attention. Lower-priority claims stay queued, balancing workflow efficiently and ensuring critical cases aren’t delayed.
Load Balancing Across Workers: Redis distributes tasks among Celery workers, balancing the load to prevent bottlenecks. This setup optimizes processing efficiency and ensures resources are used effectively, enabling the system to handle varying claim volumes seamlessly.

This asynchronous task management enables fast handling of critical claims, improving response times, and enhancing customer experience by reducing wait times and efficiently allocating resources.

Real-Time Data Flow Management with Task Manager

The task manager orchestrates task flow, dynamically assigns resources, and manages dependencies to support streamlined claims processing.

Resource Allocation: The task manager monitors task flow and assigns resources based on demand, keeping the system responsive and reducing delays during surges.
Task Sequencing for Multi-Stage Claims: In complex cases like fraud detection following policy verification, the task manager sequences tasks to ensure they are processed in the right order, maintaining accuracy.
Data Dependency Management: For workflows that rely on specific data (e.g., claims history or customer profiles), the task manager retrieves and verifies this data before advancing to subsequent stages, ensuring that each claim is assessed accurately.

By sequencing tasks and managing dependencies, this system reduces processing times and increases accuracy, enhancing the claims experience, particularly during peak times.

Real-Time Scaling for High Demand

Our system’s dynamic scaling and task distribution manage real-time increases in claim volume effectively, preventing overload. Here’s how the system responds during a claims surge:

Automated Resource Scaling: When claims volumes increase, such as during large-scale travel disruptions, Kubernetes scales up containers to meet demand in real time, keeping resources aligned with volume.
Efficient Task Routing: The task manager directs tasks to available containers or Celery workers, ensuring smooth load distribution and consistent system performance.
Controlled Task Flow with Redis: Redis manages task flow, processing tasks in order and balancing workloads to reduce bottlenecks, ensuring efficient operations even during high-volume scenarios.

This setup enables insurers to maintain timely claims processing during surges, supporting customer satisfaction and operational efficiency.

Ancileo’s Dynamic Scaling and Task Management Approach for Travel Insurers

Operational Efficiency: Asynchronous task management and load balancing optimize processing, enabling insurers to manage high claim volumes smoothly.
Cost Savings Through Scalability: By scaling resources on demand, the system keeps operational costs low while delivering consistent performance.
Enhanced Customer Experience: High-priority claims are addressed immediately, ensuring timely service and satisfaction even during demand peaks.

With cloud-based scaling, Celery, and Redis for asynchronous task management, Ancileo’s system empowers travel insurers to scale as needed, prioritize tasks, and manage complex workflows. This robust, adaptable architecture is built to handle high-volume claims processing efficiently and reliably.

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