Build a High-Accuracy Insurance Ticket Classifier with Claude AI

In the insurance industry, customer support teams face a constant stream of inquiries ranging from billing questions to complex claims assistance. Manually categorizing these tickets is time-consuming and error-prone. In this guide, you'll learn how to build a sophisticated classification system using Claude AI that achieves 95%+ accuracy, handling complex business rules and providing explainable results.

Why Use Claude for Classification?

Large Language Models like Claude have revolutionized classification tasks, particularly in scenarios where traditional machine learning struggles:

• Complex business rules: Insurance categories often involve nuanced distinctions that require understanding context
• Limited training data: You can achieve high accuracy with relatively few examples
• Natural language explanations: Claude can justify its classifications, increasing transparency
• Flexibility: Easy to update categories without retraining entire models

Prerequisites and Setup

Before we begin, ensure you have:

• Python 3.11+ installed
• An Anthropic API key (available at console.anthropic.com)
• Basic familiarity with Python and classification concepts

Install the required packages:

pip install anthropic pandas scikit-learn numpy

Set up your API key:

import anthropic
import os
Set your API key (use environment variables in production!)
os.environ["ANTHROPIC_API_KEY"] = "your-api-key-here"
client = anthropic.Anthropic(api_key=os.environ["ANTHROPIC_API_KEY"])

Understanding the Problem: Insurance Support Tickets

Insurance companies typically receive tickets across several categories. For this guide, we'll work with 10 synthetic categories generated by Claude 3 Opus:

• Billing Inquiries - Questions about invoices, charges, and payments
• Policy Administration - Policy changes, renewals, and updates
• Claims Assistance - Claims process and documentation help
• Coverage Explanations - What's covered under specific policies
• Rate and Quote Requests - New policy pricing inquiries
• Document Requests - Policy documents and forms
• Agent Support - Questions for specific agents or brokers
• Technical Issues - Website, app, or portal problems
• Complaints and Escalations - Formal complaints and escalations
• General Information - Non-urgent general questions

Step 1: Basic Classification with Prompt Engineering

Let's start with a simple classification approach using prompt engineering:

def classify_ticket_basic(ticket_text, categories):
    """Basic classification using prompt engineering"""
    
    categories_text = "\n".join([f"{i+1}. {cat['name']}: {cat['description']}" 
                                 for i, cat in enumerate(categories)])
    
    prompt = f"""You are an insurance support ticket classifier. 
    Categorize the following customer message into one of these categories:
    
    {categories_text}
    
    Customer message: {ticket_text}
    
    Return ONLY the category number (1-10) and nothing else."""
    
    response = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=10,
        temperature=0,
        messages=[{"role": "user", "content": prompt}]
    )
    
    return response.content[0].text.strip()
Example usage
categories = [
    {"name": "Billing Inquiries", "description": "Questions about invoices, charges, fees"},
    {"name": "Policy Administration", "description": "Policy changes, updates, cancellations"}
    # ... add all 10 categories
]
ticket = "I need help understanding the charges on my latest invoice"
result = classify_ticket_basic(ticket, categories)
print(f"Classified as category: {result}")

This basic approach typically achieves 70-80% accuracy. The key limitation is that Claude has no context about your specific business rules or historical classification patterns.

Step 2: Improving Accuracy with Few-Shot Examples

Adding examples dramatically improves accuracy. Here's how to implement few-shot learning:

def classify_ticket_fewshot(ticket_text, categories, examples):
    """Classification with few-shot examples"""
    
    categories_text = "\n".join([f"{i+1}. {cat['name']}: {cat['description']}" 
                                 for i, cat in enumerate(categories)])
    
    examples_text = "\n".join([f"Example: {ex['text']}\nCategory: {ex['category']}" 
                               for ex in examples[:3]])  # Use 2-3 examples
    
    prompt = f"""You are an insurance support ticket classifier. 
    
    Categories:
    {categories_text}
    
    Here are some examples:
    {examples_text}
    
    Now classify this new ticket:
    Customer message: {ticket_text}
    
    Return ONLY the category number (1-10) and nothing else."""
    
    response = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=10,
        temperature=0,
        messages=[{"role": "user", "content": prompt}]
    )
    
    return response.content[0].text.strip()
Prepare your examples
examples = [
    {"text": "Why was I charged $50 extra this month?", "category": "1"},
    {"text": "I want to add collision coverage to my policy", "category": "2"},
    {"text": "How do I file a claim for water damage?", "category": "3"}
]

This approach can boost accuracy to 85-90%. The challenge is selecting the right examples for each query.

Step 3: Implementing Retrieval-Augmented Generation (RAG)

RAG helps Claude access relevant historical examples dynamically. Here's a simplified implementation:

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
class TicketClassifierRAG:
    def __init__(self, training_data, categories):
        """Initialize with training data and categories"""
        self.training_data = training_data  # List of dicts with 'text' and 'category'
        self.categories = categories
        
        # In production, use proper embeddings like VoyageAI or OpenAI
        # For simplicity, we'll use TF-IDF here
        from sklearn.feature_extraction.text import TfidfVectorizer
        self.vectorizer = TfidfVectorizer()
        self.training_vectors = self.vectorizer.fit_transform(
            [item['text'] for item in training_data]
        )
    
    def find_similar_tickets(self, query_text, k=3):
        """Find k most similar historical tickets"""
        query_vector = self.vectorizer.transform([query_text])
        similarities = cosine_similarity(query_vector, self.training_vectors)[0]
        
        # Get indices of top k similar tickets
        top_indices = np.argsort(similarities)[-k:][::-1]
        
        return [self.training_data[i] for i in top_indices]
    
    def classify(self, ticket_text):
        """Classify using RAG"""
        # Find similar examples
        similar_tickets = self.find_similar_tickets(ticket_text, k=3)
        
        categories_text = "\n".join([f"{i+1}. {cat['name']}: {cat['description']}" 
                                     for i, cat in enumerate(self.categories)])
        
        examples_text = "\n".join([
            f"Example: {ticket['text']}\nCategory: {ticket['category']}" 
            for ticket in similar_tickets
        ])
        
        prompt = f"""You are an insurance support ticket classifier. 
        
        Categories:
        {categories_text}
        
        Here are similar historical tickets and their categories:
        {examples_text}
        
        Now classify this new ticket:
        Customer message: {ticket_text}
        
        Return ONLY the category number (1-10) and nothing else."""
        
        response = client.messages.create(
            model="claude-3-sonnet-20240229",
            max_tokens=10,
            temperature=0,
            messages=[{"role": "user", "content": prompt}]
        )
        
        return response.content[0].text.strip()
Initialize the classifier
training_data = [
    {"text": "Invoice charge question", "category": "1"},
    {"text": "Need to update my policy", "category": "2"},
    # ... more training examples
]
classifier = TicketClassifierRAG(training_data, categories)
result = classifier.classify("Why is my premium higher this month?")
print(f"RAG classification: {result}")

RAG typically achieves 90-95% accuracy by providing contextually relevant examples.

Step 4: Adding Chain-of-Thought for Explainable Results

For production systems, explanations are crucial. Here's how to add reasoning:

def classify_with_explanation(ticket_text, categories, examples):
    """Classification with chain-of-thought reasoning"""
    
    categories_text = "\n".join([f"{i+1}. {cat['name']}: {cat['description']}" 
                                 for i, cat in enumerate(categories)])
    
    prompt = f"""You are an insurance support ticket classifier. 
    
    Categories:
    {categories_text}
    
    Analyze this customer message step by step:
    1. Identify the main topic and keywords
    2. Determine which category best matches
    3. Explain your reasoning
    4. Provide the final category number
    
    Customer message: {ticket_text}
    
    Format your response as:
    Analysis: [your analysis]
    Reasoning: [your reasoning]
    Category: [number only]"""
    
    response = client.messages.create(
        model="claude-3-sonnet-20240229",
        max_tokens=300,
        temperature=0,
        messages=[{"role": "user", "content": prompt}]
    )
    
    return response.content[0].text

Step 5: Testing and Evaluation

Always test your classifier systematically:

import pandas as pd
from sklearn.metrics import accuracy_score, classification_report
def evaluate_classifier(classifier_func, test_data):
    """Evaluate classifier performance"""
    predictions = []
    actuals = []
    
    for item in test_data:
        predicted = classifier_func(item['text'])
        actual = item['category']
        
        predictions.append(predicted)
        actuals.append(actual)
    
    accuracy = accuracy_score(actuals, predictions)
    report = classification_report(actuals, predictions)
    
    print(f"Accuracy: {accuracy:.2%}")
    print("\nClassification Report:")
    print(report)
    
    return accuracy, predictions
Load your test data
test_data = pd.read_csv("test_tickets.csv")  # Should have 'text' and 'category' columns
Evaluate
evaluate_classifier(classifier.classify, test_data.to_dict('records'))

Production Considerations

When deploying to production:

• Implement caching: Cache similar ticket embeddings to reduce API calls
• Add fallback logic: For low-confidence predictions, route to human review
• Monitor drift: Regularly test with new data to detect accuracy degradation
• Implement batching: Process multiple tickets in parallel when possible
• Add logging: Log predictions and confidence scores for continuous improvement

Key Takeaways

• Start simple, then iterate: Begin with basic prompt engineering (70-80% accuracy), then add few-shot examples (85-90%), and finally implement RAG (90-95%+)

• Context is crucial: Claude's performance improves dramatically with relevant examples. RAG provides dynamic context based on similarity to historical tickets

• Explainability matters: Use chain-of-thought prompting to get reasoning behind classifications, which builds trust and helps with debugging

• Test systematically: Always evaluate with a proper test set and track accuracy, precision, and recall for each category

• Production requires planning: Implement caching, fallbacks, monitoring, and logging to ensure reliability in real-world applications

By following this guide, you can build a production-ready insurance ticket classifier that handles complex business rules, works with limited data, and provides transparent, explainable results.