Claims handling is one of the most complex and high-stakes processes in insurance. Adjusters are tasked with combing through massive amounts of unstructured information without the right tools to turn it into actionable insights. The outcome is often longer cycle times, inconsistent decisions, and rising costs, especially amid record catastrophic events.

This increasing complexity is reflected in the data: J.D. Power reported that homeowner claims satisfaction fell to a seven-year low in 2024, with delays and extended repair times cited as major frustrations.

However, applying artificial intelligence (AI) to claims processing can boost efficiency by up to 50% while reducing operational costs by 20%. For claims organizations that can translate this potential into reality, analytics-driven claims knowledge solutions give adjusters real-time intelligence, historical references, and data-backed recommendations — helping them cut through information overload to make faster, more consistent decisions.

What’s Holding Back Claims Analytics for Insurance?

In the world of claims handling, adjusters are under increasing pressure to deliver fast, fair, and accurate outcomes. Yet they remain saddled with three interconnected burdens:

1. Unstructured Data Overload

Most of the information flowing through claims isn’t neatly structured in database fields. Instead, it arrives as images, free-form notes, or scanned documents that are difficult to analyze at scale. Being able to process unstructured data is a key element to improving claims processing functions.

2. Fragmented Systems

Critical claim data often lives in separate systems and archives, forcing adjusters to piece together information manually. This fragmentation is widespread: A 2024 survey found that 38% of organizations report being “completely or very” siloed, with another 31% “somewhat” siloed. For insurers, these silos slow analysis, increase errors, and make it harder to deliver consistent, timely claim outcomes.

3. Reliance on Individual Expertise

In many organizations, claims outcomes still depend heavily on an adjuster’s personal experience rather than standardized knowledge. This creates variability that can lead to inconsistent decisions, uneven settlement times, and higher exposure to litigation. For instance, one adjuster may quickly identify a type of red flag that another adjuster chooses to overlook. Without a way to capture and share institutional expertise, carriers risk perpetuating these inconsistencies across every new claim.

These challenges impact both financial performance and customer trust. Longer cycle times drive up operating costs, while inconsistent decisions and missed insights contribute directly to leakage, litigation expenses, and customer frustration. Without new ways to turn data into actionable context, carriers risk widening the gap between what’s expected and what they can deliver, with profitability and retention on the line.

The Analytics-Driven Claims Knowledge Solution

Adjusters need more than fragmented systems and personal intuition. Analytics-driven claims knowledge solutions close this gap by consolidating structured and unstructured data into a knowledge base and layering on advanced analytics. Rather than starting from scratch with each case, adjusters gain real-time access to historical outcomes, contextual insights, and recommendations that improve both speed and consistency.

Consider a bodily injury claim with disputed liability, for instance. An insurance claims analytics system can flag that similar cases historically had a high probability of escalation, allowing the adjuster to initiate early negotiations and reduce litigation exposure. When repeated across thousands of claims, these improvements add up.

Here are seven steps to create an analytics-driven claims knowledge solution and improve the claims experience for adjusters and policyholders.

1. Capture and Convert Claims Data Into Structured Insights

Begin by collecting multimodal claims data — photos, adjuster notes, call transcripts, first notice of loss (FNOL) forms, medical narratives, and documents. Apply natural language processing (NLP), optical character recognition (OCR), and image-to-text models to extract attributes such as injury type, loss cause, vehicle damage patterns, sentiment, and inconsistencies. Standardize these insights into a unified schema that can be indexed and reused. This creates the foundational layer of clean, machine-readable information.

2. Encode Claims Data Into Vector Representations

Transform all structured and unstructured data into vector embeddings using domain-tuned large language models (LLMs) and image encoders. This allows the system to detect nuanced similarities across millions of past cases — patterns too subtle for rule-based systems. Ensure embeddings are stored in a scalable vector database to support fast, high-accuracy retrieval.

3. Build Contextual Knowledge Retrieval Pipelines

Create retrieval workflows that automatically pull the most relevant historical cases, guidelines, and outcomes based on the adjuster’s query or the claim’s current attributes. Integrate retrieval-augmented generation (RAG) pipelines to provide context-aware explanations grounded in real claim precedents. This becomes the single source of truth for claims insights and reference material.

4. Enable Real-Time Natural Language and Semantic Search

Deploy an intuitive search interface where adjusters can ask natural questions, such as “What’s the typical litigation risk for roof damage with a public adjuster involved?”. Use semantic search and retrieval models to instantly return precise, evidence-backed results. Integrate this into core claims applications like Guidewire, Duck Creek, or legacy systems for seamless use within adjusters’ daily workflows.

5. Deliver Intelligent Next-Best-Action Recommendations

Leverage the company’s claim handling policy, historical outcomes, and pattern recognition to suggest recommended actions: early settlement, escalation to the company’s Special Investigations Unit (SIU), legal consultation, documentation requests, or claimant outreach. Tie recommendations to the claim context, risk score, and adjuster notes to ensure they are actionable and defensible. This turns insights into operational guidance rather than just information.

6. Integrate Continuous Learning and Feedback Loops

Capture adjuster feedback, outcome data, litigation results, and action selections to continually refine the models. Implement advanced monitoring and alert mechanisms for data drift detection as well as model re-training. Over time, this strengthens the system’s accuracy and ensures it stays aligned with business goals and market trends.

7. Embed the AI Assistant Into Adjuster Workflows

Make the system a natural extension of the adjuster’s workflow by integrating it into claim files, decision screens, and documentation tools. Provide concise summaries, case comparisons, and recommendations at the moment of decision.

Claims analytics solutions are not about replacing adjusters; they are about augmenting them with intelligence at scale. By transforming unstructured data into actionable insights, these tools reduce variability in decision-making, shorten cycle times, and improve consistency across the claims workforce.

The Next Chapter in Claims Handling

Equipping adjusters with analytics-driven digital co-pilots ensures institutional expertise is preserved, decisions are backed by data, and customer experience improves. In a business where margins are thin and trust is critical, empowering claims teams with leading capabilities is the path toward the next chapter of claims handling.

Ready to learn how real insurers are benefiting from investing in the adjuster experience? Watch our on-demand webinar, “Ask the Experts: Boosting Claims Efficiency & Effectiveness Through Intelligent Automation.”