pashas/insurance-ai-reliability-benchmark

Insurance AI Agent Reliability Benchmark

The first standardized benchmark for AI agents in insurance.

510 test scenarios. 10 categories. One question: Can your AI handle real insurance workflows?

Why This Exists

Insurance AI agents must be reliable. There is no room for error.

A wrong routing decision delays a claim. A missed compliance flag triggers regulatory action. A failed escalation harms a vulnerable customer.

General chatbot benchmarks do not test for this. No standard benchmark existed for insurance AI agents.

This dataset fills that gap.

Built from patterns observed in production voice AI systems for insurance. Every scenario reflects a real workflow.

What It Tests

510 scenarios across 10 categories test four things:

1. Intent recognition -- Does the AI understand what the customer needs?
2. Routing decisions -- Does the AI send the request to the right place?
3. Action completeness -- Does the AI take every required step?
4. Response quality -- Does the AI respond clearly and correctly?

Categories

Category	Count	Description
claim_intake	80	New claim filing
claim_status	60	Claim status inquiries
policy_inquiry	70	Coverage questions
payment_processing	50	Billing and payments
policy_change	60	Policy modifications
document_request	40	Document requests
escalation_trigger	50	Human handoff triggers
error_recovery	40	Error handling
multi_turn_workflow	30	Complex multi-step flows
edge_cases	30	Unusual and adversarial inputs

Routing Decisions

Each scenario maps to one of four routing decisions:

Decision	Meaning
ai_handle	AI handles the request fully
ai_with_verification	AI handles it, human verifies
human_handoff	Transfer to a human agent
hybrid_collaborative	AI and human work together

Insurance Lines Covered

• Personal Auto
• Homeowners
• Renters
• Life
• Health
• Commercial General Liability
• Workers Compensation
• Professional Liability
• Cyber Liability

Difficulty Distribution

Level	Share	Description
Easy	~40%	Clear intent. Standard workflow.
Medium	~35%	Ambiguous input. Requires clarification.
Hard	~25%	Complex, regulatory, or adversarial.

Evaluation Metrics

Five metrics measure agent performance:

Metric	What It Measures
Intent Accuracy	Correct intent classification
Routing Correctness	Correct routing decision
Action Completeness	All required actions identified (0-1 score)
Response Quality	Rubric-based evaluation
Latency Compliance	Response within acceptable time

Quick Start

from datasets import load_dataset

dataset = load_dataset("pashas/insurance-ai-reliability-benchmark")

# Access splits
train = dataset["train"]         # 357 scenarios
validation = dataset["validation"]  # 76 scenarios
test = dataset["test"]           # 77 scenarios

# Look at a scenario
scenario = train[0]
print(f"Category: {scenario['category']}")
print(f"Customer: {scenario['input']['customer_message']}")
print(f"Expected routing: {scenario['expected_output']['routing_decision']}")

Dataset Splits

Split	Examples	Purpose
Train	357	Model development and fine-tuning
Validation	76	Hyperparameter tuning
Test	77	Final evaluation

Schema

Each scenario includes:

• id -- Unique scenario identifier
• category -- One of 10 test categories
• subcategory -- Specific workflow within the category
• difficulty -- Easy, Medium, or Hard
• input -- Customer message and context
• expected_output -- Correct intent, routing, actions, and response
• evaluation_criteria -- Scoring rubric for the scenario
• metadata -- Insurance line, regulatory flags, and additional context

Related Work

• Hybrid Orchestrator Framework: github.com/pavelsukhachev/hybrid-orchestrator
• Research paper: "Hybrid Human-AI Orchestration: Design Patterns from a Production Voice AI System" (TechRxiv/IEEE)

Citation

@dataset{sukhachev2026insurance,
  title={Insurance AI Agent Reliability Benchmark},
  author={Sukhachev, Pavel},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/pashas/insurance-ai-reliability-benchmark}
}

License

Apache 2.0

Author

Pavel Sukhachev

• Email: pavel@electromania.llc
• LinkedIn: linkedin.com/in/pavelsukhachev
• ORCID: 0009-0006-4546-9963