From Rules to Reasoning: Why AI Is the Future of Pet Claims Processing

Why tradditional rule-based automation has failed to deliver on its promise

Rules-based automation systems follow a simple principle: “If X, then Y.” While these approaches are effective for standardized, high-volume tasks (like address verification or simple benefit calculations), they struggle in the face of ambiguity, variation, and real-world data complexity.

Here’s why they fall short in insurance:

1. They can’t handle unstructured data
   Pet insurance claims are filled with vet records, handwritten notes, scanned invoices, images, and free-text communication. Rules-based systems depend on clean inputs and predefined formats. But real claims don’t come that way.
2. They break down at scale
   Even small deviations—like a missing field or handwritten note—can break a rules engine. That leads to manual intervention, slowing down processing and increasing cost.
3. ‍They lack context awareness
   ‍Rules don’t understand nuance. They can’t connect a pattern of repeated treatments to a potential chronic condition, or recognize when an invoice amount is out of range for a particular breed and age.

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The AI Leap: From Extraction to Intelligence

Today’s insurance-grade AI models are built to do what rules can’t: interpret, learn, and act within complex real-world conditions.

What’s changed?

• AI now reads beyond OCR. It understands handwritten notes, complex PDF layouts, and embedded tables with accuracy that matches or exceeds human reviewers.
• It interprets context, linking structured data (e.g., policy info) with unstructured inputs (e.g., vet records) to identify preexisting conditions, fraud risks, and policy coverage.
• It gets smarter over time, adapting to new document types, regional pricing variations, or emerging fraud signals without needing to be re-coded.

Key Benefits of AI in Claims Automation

1. Increased Operational Efficiency
   Claims handlers spend 30% of their time on low-value tasks like reading documents. AI automates that, freeing up time for strategic, high-touch cases while accelerating the rest.
2. Improved Policyholder Experience
   A major driver of dissatisfaction in pet insurance claims is slow settlement. AI changes that. With AI adjudication, claims that once took weeks can now be processed in minutes, even at the point of care.
3. Higher Accuracy and Reduced Leakage
   AI models trained on millions of data points can detect inconsistencies and fraud signals humans miss — like reused photos, duplicate invoices, or outlier treatment pricing for a given breed and region. The result is more accurate payouts and lower loss ratios, without sacrificing speed.
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‍No More Trade-Offs Between Automation and L/R

Legacy claims automation forced insurers to sacrifice accuracy for speed. With insurance-trained AI, pet insurers no longer have to choose—claims can now be processed in real time, with greater accuracy, less manual work, and better fraud detection. AI isn’t just an upgrade—it’s the new standard for modern claims operations