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When developing Lea’s AI claims module, we faced challenges like outdated legacy systems, inconsistent data formats, and evolving fraud tactics.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.
Key Learnings
In AI-driven claims processing, travel insurance demands a highly adaptive and cost-conscious system. Ancileo’s approach provides dynamic scaling, specialized processing, and AI model optimization to handle fluctuating claims volumes, especially during events like natural disasters. This tailored system delivers both performance and cost efficiency.
On-Demand Resource Scaling
To handle fluctuating claim volumes, our system scales resources in real time based on incoming workload. During events such as a major flight delay or natural disaster, claims spike dramatically. Our infrastructure immediately scales up inference pods for AI tasks, like document verification and fraud detection. When volumes drop back to normal, these pods scale down to prevent unnecessary costs.
Task-Specific Pods for Targeted Processing
We assign dedicated pods for distinct claim-processing tasks, improving efficiency and ensuring that each task type has the optimal resources.
Example: After a widespread flight cancellation, the system scales up inference pods to handle increased claims submissions, prioritizing document verification to quickly assess eligibility. This scaling allows timely processing while avoiding overuse of system resources.
Quantization reduces computational demands by simplifying data, making AI models more efficient without compromising performance.
Precision Reduction for Optimized Processing
By converting high-precision floating-point data (e.g., 32-bit) to lower-precision (e.g., 8-bit), our system reduces the computational intensity required for AI model operations. This is especially beneficial for processing large data volumes rapidly, which lowers costs during high-demand periods.
Binary Encoding for Efficient Model Execution
Binary encoding compresses model parameters, enabling faster model inference. This method supports high-priority claims processing where accuracy is essential, yet speed and cost-efficiency are required.
Example: For a flagged high-cost medical claim in a remote area, the system deploys a quantized model to evaluate document authenticity and potential fraud indicators. This use of a cost-efficient model allows rapid decision-making without inflating costs.
Our hybrid infrastructure combines cloud scalability with in-house servers, managing costs effectively while remaining responsive to demand.
Routine On-Premises Processing, Cloud for Demand Surges
Regular claims are processed on in-house servers, providing a stable, low-cost baseline. In cases of demand surges—such as after a natural disaster—the system activates additional cloud resources, ensuring we meet volume needs without overspending.
Example: Following a hurricane, our cloud resources are engaged to manage the influx of claims, such as emergency medical assistance or trip cancellations. Once claims volume stabilizes, the system reverts to in-house processing, maintaining cost control.
Inference tasks, like detecting document anomalies or verifying claim details, require considerable resources. Efficient management of these processes is crucial for keeping costs manageable.
On-Demand Inference Pod Activation
Inference pods activate only when specific tasks, such as real-time fraud detection, are needed. This prevents continuous use of high-cost resources and keeps operational expenses aligned with demand.
Machine Learning as a Service (MaaS) for Shared Resources
Using MaaS, we run certain inference tasks on shared models instead of dedicated infrastructure, reducing costs without sacrificing availability. This model is ideal for cost-sensitive operations where full-time resources aren’t necessary.
Example: When a claim triggers fraud indicators, the system activates a shared MaaS-based inference model to validate anomalies. This approach keeps costs low by utilizing shared AI resources while maintaining processing accuracy.
During high-demand periods, quantized models allow the system to manage claim surges efficiently, combining speed with cost savings.
Binary Optimization for Cost Management
Quantized models are deployed in inference pods during peak periods to accelerate predictions while reducing the computational load, balancing speed with reduced costs.
Example: In a sudden claims influx after a major travel disruption, quantized models process claims rapidly, lowering costs associated with high-volume processing and ensuring claims assessments continue seamlessly.
Ancileo’s resource management approach is tailored to the unique demands of travel insurance, providing cost-effective solutions with dynamic resource allocation and a flexible infrastructure.
With a carefully balanced approach that combines on-demand scaling, optimized AI models, and hybrid infrastructure, Ancileo’s system offers travel insurers a cost-effective, high-performing solution. This setup meets the demands of AI-driven claims processing, enhancing operational reliability and financial efficiency.