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Overview

Artificial intelligence systems used in insurance claims handling depend heavily on the quality of the data they receive.

From photographs and written descriptions to contractor estimates and third-party inputs, AI-driven decisions are only as reliable as the information provided.

When data is incomplete, inaccurate, or misinterpreted, the resulting claim outcomes may be flawed — even if the system itself is functioning as designed.

This creates a growing area of regulatory concern: data quality and input validation in AI-driven claims handling.

The Emerging Risk

AI systems are designed to process and evaluate large volumes of data efficiently. However, they often assume that incoming data meets a minimum threshold of quality and reliability.

In practice, claim inputs may include:

• low-quality or incomplete photographs

• vague or inconsistent descriptions of damage

• inaccurate or inflated contractor estimates

• missing or partial documentation

• conflicting information from multiple sources

Without proper validation, these inputs can lead to incorrect conclusions.

Why Regulators Will Care

Departments of Insurance (DOIs) and regulatory bodies focus on whether claims are:

• handled accurately

• evaluated fairly

• supported by appropriate documentation

If claim decisions are based on unreliable or insufficient data, regulators may question:

• the integrity of the evaluation process

• whether reasonable investigation standards were met

• whether the insured received fair consideration

This may raise concerns related to:

• Unfair Claims Settlement Practices

• inadequate investigation

• improper claim determination

The Validation Gap

Traditional claims handling relies on human judgment to assess data quality.

Adjusters typically:

• request additional documentation when needed

• question unclear or inconsistent information

• physically inspect damages when necessary

AI systems, however, may:

• accept inputs at face value

• lack the ability to fully assess context

• fail to recognize when information is insufficient

This creates a validation gap between data received and decisions made.

Consequences of Poor Data Quality

When AI systems rely on unvalidated inputs:

• damages may be underestimated or overstated

• coverage decisions may be based on incomplete facts

• claims may require reopening or re-evaluation

• disputes and complaints may increase

Even when the system operates correctly, poor input quality can produce unreliable outcomes.

Link to Failure Scenario

This risk is illustrated in the Failure Library scenario:

“Failure to Validate Data Inputs Used in AI Claim Decisions”

In that scenario:

• the AI system processes incomplete or unclear data

• no additional information is requested

• the claim decision is based on insufficient inputs

This demonstrates how data quality directly impacts claim accuracy.

Regulatory Risk Indicators

Carriers implementing AI in claims handling should monitor for:

• High rates of claim re-evaluation or reopening

• Frequent disputes related to damage assessment

• Limited documentation supporting claim decisions

• Acceptance of low-quality or incomplete inputs

• Lack of data validation protocols

These indicators may signal weaknesses in input validation processes.

Gold Standard Approach

To mitigate data quality risk, carriers should implement robust validation controls.

1. Establish Data Quality Standards

Define minimum requirements for images, documentation, and descriptions.

2. Validate Inputs Before Decision-Making

Ensure that data meets quality thresholds before it is used in evaluation.

3. Request Additional Information

Prompt for clarification or supplemental data when inputs are insufficient.

4. Integrate Human Oversight

Route claims with questionable or incomplete data to human adjusters.

ClaimSurance Insight

AI does not eliminate the need for investigation — it depends on it.

A system that accepts all inputs without validation risks producing decisions that are technically efficient, but fundamentally flawed.

Data quality is not a secondary concern — it is the foundation of reliable claims handling.

Bottom Line

As AI becomes more prevalent in claims operations, regulators will expect carriers to demonstrate that claim decisions are based on accurate and sufficient information.

The key question will be:

Was the decision based on reliable data?

If the answer is uncertain, the integrity of the claim — and the system behind it — may be called into question.