Building Trust in AI: Ensuring Reliable Outcomes in Claims Management

As the insurance sector navigates increasing complexity, AI tools are emerging as the cornerstone of efficient and effective claims management. Large Language Models (LLMs), a groundbreaking form of AI, are at the forefront of this transformation, offering unparalleled potential to streamline insurance claims management.

From summarizing extensive claim documents to delivering a comprehensive analysis of claim details, LLMs have the power to alter the way insurers handle claims. But as insurance providers are starting to embrace AI innovations, one critical question arises: How can we trust the outputs generated by these AI tools?

Credibility is the foundation of successful claims management, where errors can impact profitability, compliance, and most importantly, customer trust. In this blog, we explore the steps insurers can take to make sure their claims AI is a trusted partner.

AI That Understands Humans and Talks Like Them

LLMs are advanced AI models designed to process and generate natural language text with remarkable accuracy. Trained on enormous datasets, they have the ability to comprehend nuanced queries, summarize documents, and create human-like responses.

LLMs are improving operations across every industry: In healthcare, they summarize patient records and suggest diagnoses. In the legal field, LLMs draft contracts, analyze case law, and generate detailed research summaries. In customer support they automatically solve tickets as well as help support engineers getting to the root cause faster. 

As industries continue to evolve, LLMs are becoming indispensable tools for enhancing efficiency and decision-making. The insurance sector, historically reliant on manual processes, is now tapping into this potential to reimagine claims management.

How LLMs Impact Insurance Claims Management

Insurance claims management is an intricate process that demands speed, accuracy, and attention to details. LLMs are redefining the role of adjusters and claims managers by automating manual and repetitive tasks, offering real-time insights, and enabling adjusters to focus on what matters most – complex decision-making and customer service.

Applications of LLMs in Claims Management

• Document Automation: LLMs can generate essential claims documentation, such as claim summaries, letters to claimants or vendors, and follow-up emails, reducing manual workloads and eliminating errors.
• Data Extraction and Summarization: Adjusters spend significant time sifting through complex claim files, adjuster notes, and incident reports. LLMs can quickly extract key information and present it in concise, actionable formats.
• Decision Support: By analyzing historical claims data, LLMs provide adjusters with recommendations for claim resolutions, including optimal settlement amounts, vendor suggestions, or next steps in complex claims.
• Enhanced Communication: LLM-powered chatbots and automated email systems ensure policyholders receive accurate and timely responses, improving customer satisfaction during critical moments.

The integration of LLMs into claims management systems accelerates processes, reduces costs, and helps adjusters battle the daily workloads with ease. 

The Risks of LLMs: Understanding AI Hallucinations

While LLMs offer numerous advantages, they also come with challenges. A major concern is “AI hallucinations”, where models produce plausible but incorrect outputs. Publicly available LLMs, like ChatGPT, are often used by adjusters to manage daily workloads but lack the domain-specific knowledge and contextual understanding needed for claims management. 

Adjusters using public LLMs face serious risks to the claims management process, including:

• Misinterpretation of Claim Data: If the AI misreads or fabricates details from claim documents, it could lead to incorrect resolutions.
• Compliance Failures: Erroneous outputs could inadvertently violate industry regulations or company policies.
• Customer Dissatisfaction: Mistakes caused by hallucinations can erode trust and damage brand reputation.

These risks highlight the need for strong guardrails to ensure LLM outputs are credible, accurate, and aligned with regulatory requirements.

Ensuring Credibility in LLM Outputs: Steps for Success in AI Claims Management 

To fully capitalize on LLMs while mitigating risks, insurers must adopt a systematic approach to ensure credibility:

Set Clear Expectations

• Define the scope of LLM-generated outputs, such as document summaries, settlement recommendations, or automated customer communication.
• Ensure the AI’s training aligns with the specific needs of insurance claims management.

Create a Validation Framework

Develop a checklist to evaluate the accuracy and relevance of AI outputs, and run it to check against the answers received. This ensures that the outputs align with expectations and meet the necessary standards. Key questions to include in the checklist are:

• Does the output address the query or task correctly?
• Are references to claim documents accurate?
• Does the output comply with company policies and industry regulations?

Implement Audit and Feedback Loops

Continuous monitoring and refinement is key to keep AI systems safe and useful in the long-run. In order to do so, one has to:

• Regularly sample and review LLM-generated outputs for quality assurance.
• Collect feedback from adjusters and managers to identify recurring errors or areas for improvement.
• Use this feedback to refine AI models and enhance their performance over time.

Laying a Secure Foundation for LLM Deployment

The platform on which an LLM operates is just as critical as the AI itself. Insurers must prioritize secure and integrated environments to ensure data integrity, compliance, and security. 

This requires LLM platforms (like Google Cloud Platform) that safeguard sensitive claims data with encryption and strict access controls, incorporate built-in checks to ensure outputs meet regulatory standards, and seamlessly integrate with operating claims management systems to enable real-time validation. 

Meet Clive™: Five Sigma’s Trusted AI Claims Adjuster

Where many AI tools fail to inspire trust, Clive™, the insurance industry’s first AI claims adjuster, stands apart. Developed by Five Sigma, a pioneer insurtech dedicated to transforming claims management with advanced technology, Clive is purpose-built for the insurance industry. 

Clive combines deep insurance expertise with cutting-edge AI, adding industry-focused AI capabilities to any existing claims management system (CMS) to automate routine tasks, dynamically plan claim handling, and advance the claim automatically according to the insurer’s operating procedure (SOP), driving artificial intelligence, efficiency, and accuracy in claims handling.

What makes Clive trustworthy?  

Many layers of security and safety go into the development of Clive. Notable examples include:

• Built on a Secure Foundation: Clive operates on Google Cloud Platform (GCP), ensuring advanced encryption, strict access controls, and enterprise-grade data security.
• AI Guardrails: Extensive safeguards are embedded to prevent errors and maintain the integrity of all AI-generated outputs.
• Insurance-Specific embedded knowledge: Clive internal knowledgebase comes with a variety of insurance data and workflows, ensuring its recommendations align with real-world claims processes.
• Internal Validation Checklists: Every output is tested against a set of constitutional layers to ensure it meets company policies and regulatory standards.
• Screening Mechanisms: Clive employs rigorous checks to detect and eliminate inappropriate content, biases, or irrelevant outputs, ensuring its responses are fair, professional, and aligned with industry standards.

Clive delivers the secure, credible, and intelligent support insurers need to optimize claims management while maintaining the trust of adjusters, managers, and customers alike.

The Future of Claims Management with AI

LLMs are enablers of a smarter, faster, and more efficient claims management process. By addressing their limitations and ensuring credibility, insurers can utilize the full potential of AI without fear of inaccuracies. 

Ready to harness the full potential of AI?