How to Protect Yourself Against AI-Driven Claim Reductions

By:
Doug Weatherman MWR, MSR, MTC
on Fri, 06/27/2025
Tech Toolbox-Technology every contractor should have in their kit. Article Name: How to protect yourself against AI-Driven Claim Reductions by Doug Weatherman

How to Protect Yourself Against AI-Driven Claim Reductions

By Doug Weatherman

The integration of Artificial Intelligence (AI) into everyday business operations has revolutionized industries ranging from healthcare to finance. In the property restoration and insurance industries, AI-driven technologies like Generative AI, Large Language Models (LLMs), and Claims Management Models (CMMs) are playing an increasingly central role. While these advancements offer significant efficiencies, they also introduce new complexities that contractors, policyholders, and restoration professionals must understand to protect their interests.

1. Generative AI and Large Language Models (LLMs)

Generative AI and Large Language Models (LLMs) like OpenAI’s GPT-4 and similar AI solutions process vast amounts of text data to generate responses, automate tasks, and analyze documents. In the insurance industry, these technologies are being deployed to:

  • Automate claims processing by analyzing policies, comparing damage reports, and possibly suggesting payout amounts.
  • Enhance adjuster decision-making by pulling from historical claim data.
  • Attempt to standardize pricing through AI-driven cost estimation models like those embedded in commonly used software solutions.

These tools can enhance efficiency and consistency in claims handling. However, as with any technology, it is essential to ensure that AI-generated estimates and recommendations align with real-world costs. AI systems rely on the data they are fed, making it crucial that this data remains accurate and representative of current conditions.

2. Claims Management Models (CMMs)

CMMs are AI-driven frameworks that insurance carriers use to assess, manage, and settle claims. These models analyze claim submissions, compare them against historical data, and predict what a reasonable payout should be. The advanced learning of the maturity rate of these machines, combined with the level of data they have from claims going back decades, poses a unique set of challenges.

CMMs:

  • Prioritize insurer cost savings by setting AI-based thresholds for claim payouts based on historical data from similar losses, using data provided by insurance
  • Automate denials and reductions by flagging specific cost line items as excessive, often without human oversight. Increasing automated fraud triggers based on systems learning, leading to claim denial automation without human oversight.
  • Introduce AI bias based on the data they are trained on, which may disproportionately favor insurer interests over policyholder needs​.

CMM machines will have the most impact on restorers by 2030, and that impact is already being felt. 

At the same time, as these models become more sophisticated, it’s essential to ensure that they remain flexible enough to account for the unique aspects of each claim. Transparency in how these models operate can help instill confidence that claims are being handled fairly.

CMMs (the citations for more information): https://research.ibm.com/blog/what-is-generative-AI,  https://www.ibm.com/think/topics/large-language-models , https://www.accenture.com/us-en/insights/artificial-intelligence/ai-maturity-and-transformation

 

3. Large and Small Multimodal Models (LMMs and SMMs)

Beyond text processing, AI-powered Large and Small Multimodal Models (LMMs and SMMs) integrate data from images, videos, and sensor data to assess property damage and evaluate claims. Insurance carriers are increasingly leveraging LMMs to:

  • Assess property damage remotely using drones and satellite imaging. Using historical data and mitigation cost per square foot averages based on varying estimates and platforms. 
  • Automate claim assessments through AI-powered photo and 3D geospatial scan analysis. 
  • Further minimization of on-site inspections by relying on pre-existing AI damage models rather than human assessment. 
  • The combination of Modals is going to produce extremely strong AI in the next couple of years. 

While these AI applications offer significant advantages in efficiency, human oversight remains essential to ensure that damage assessments are accurate and account for real-world conditions that AI may not fully capture.

Citation for more reading: https://www.ibm.com/think/topics/multimodal-ai

 

AI’s Role in the Insurance and Restoration Process

AI-driven tools help insurers create more consistent and data-driven claim evaluations. However, it’s helpful for restoration professionals and policyholders to be aware of how AI contributes to claim determinations and pricing estimates. Without sufficient transparency and oversight this technology has the ability to have a negative impact in claim covered restoration and disaster recovery. 


AI-Powered Cost Estimation

AI models use historical data and market trends to estimate costs for labor and materials. These models aim to provide fair assessments, but they depend on the accuracy of the underlying data. Key factors to consider include:

  • AI pricing models may not always reflect real-time fluctuations in material and labor costs.
  • Contractors and policyholders can provide updated market data to ensure accuracy in AI-generated estimates.
  • Collaboration between insurers, contractors, and policyholders can help ensure more accurate pricing and transparency.

AI-Assisted Claim Reviews

AI is used to help streamline the review process by auditing line items and identifying potential discrepancies. However, AI models should be used as tools to assist decision-making rather than replace human judgment.

  • If an AI model flags certain line items, policyholders and contractors can request clarification or further review.
  • Open discussions with adjusters can help resolve discrepancies and ensure fair claim outcomes.

Best Practices for Restorers and Their Clients When Engaging with AI-Driven Claims Processes

As AI continues to play a larger role in insurance and restoration, staying informed and engaged can help ensure that claims are handled fairly and transparently. As restorers, we must ensure the data we feed these models is accurate and detailed. Here are a few proactive steps policyholders and restoration professionals can take:

1. Seek Clarity and Transparency

 

  • Submit quality and complete documentation and notes in the estimate. If a line item gets flagged, you have a better chance of passing it through if you leave detailed notes for the reviewer. 

  • Keep a log of carrier/reviewers/adjusted line items, and do your interval review before sending it out.

The future of claims will demand clarity. In return, and in a perfect world, we should see more speed when it comes to claims processing. The data we produce is the food for machine learning. The advantage (or disadvantage) we have is that we, the restorers, will be the authors of the recipe.  

2. Advocate for Human Oversight

  • If an AI model generates an estimate, request a human review to verify its accuracy.
  • Recognize that AI should assist, not replace, the expertise of industry professionals.

3. Educate Policyholders

  • Help policyholders understand the use and potential impact of AI technologies.
  • Encourage open communication with insurers to resolve discrepancies amicably.

How is the RIA’s AGA Working on This Issue on Behalf of the Industry?

The Restoration Industry Association’s Advocacy and Government Affairs (AGA) Committees are actively engaged in monitoring and addressing challenges related to the growing impact of AI technologies in the insurance and restoration sectors. 

Here’s how AGA is tackling this challenge:

Monitoring AI Integration in Claims Processes
The AGA is closely monitoring how AI tools are being deployed—such as pricing algorithms, Claims Management Models (CMMs), and image analysis platforms—and working collaboratively to advocate for human oversight and transparency in AI-assisted decision-making.

Promoting Further Evaluation of AI Data to Account for Market Conditions & Variables
AI generated estimates and data may not accurately reflect market conditions or take into account project variables. The AGA is advocating for language to be included in AI-based software user license agreements acknowledging that data provided through these tools represents a reference point for cost estimation that requires further evaluation and is subject to modification as necessary to account for market conditions, material costs and project variables. 

Educating Industry Professionals
The AGA equips restoration professionals with information to understand the impact of AI-based software and education on how to provide thorough and accurate documentation to minimize pushback and potential discrepancies.

Building Industry Consensus
The AGA fosters collaboration within the restoration industry to build a unified voice to support transparency and human oversight of AI-assisted decision-making. The AGA regularly engages with restoration companies, software providers, and industry leaders to represent the collective concerns of restorers on these issues.

Conclusion: Embracing AI While Ensuring Fairness

AI has the potential to enhance efficiency, accuracy, and consistency in the insurance and restoration industries. When used responsibly, these technologies can streamline claims processing and improve outcomes for all parties involved. However, maintaining a balance between AI-driven automation and human expertise is key to ensuring fairness and accuracy.

By staying informed, asking the right questions, and advocating for transparency, restoration professionals and policyholders can navigate AI-driven decision-making in the claims process with confidence. Open dialogue and collaboration between insurers, contractors, and consumers will help ensure that AI serves as a beneficial tool rather than a point of contention.