From Legacy Systems to AI: The Technology that Claims Adjusters Need Now

Insurance Claims at a Crossroads

Insurance claims are where promises meet reality, making them a pivotal moment for insurers and policyholders alike. Adjusters, at the heart of this moment, face increasing workloads, rising customer expectations, and the constraints of outdated claims management systems. These challenges leave adjusters struggling to process claims efficiently, meet customers’ expectations, and maintain accuracy.

AI-driven tools are stepping in to transform the insurance claim’s journey. By automating workflows, offering real-time insights, and reducing administrative burdens, these innovations create a seamless claims management experience. The result? Adjusters can focus on what they do best— resolving claims while delivering exceptional customer service.

The Struggle with Legacy Claims Management Systems

Despite the digital transformation seen across many industries, the insurance sector has been slower to modernize its claims management systems by incorporating AI into their workflows. Chief claims officers may be concerned about losing control and the impact of AI on their claims operations. Research shows that up to 74% of insurance companies still depend on legacy claims management systems for core functions. 

Currently, many adjusters still work with legacy claims technology. Such systems are less effective in their ability to consolidate and utilize claims information and data—especially scattered, disorganized data, often leading to inefficiencies, increased error rates, and missed opportunities to improve decision-making and customer service. As a result, adjusters face challenges in accessing critical information quickly, making informed decisions, and delivering a seamless claims experience.

Outdated claims management systems often lead to frustrating bottlenecks:

• Scattered Claims Data: Claims data is often scattered across multiple platforms, such as different databases for medical records, policy details, and communication logs. Most of this claims data is unformatted, making it difficult to search, utilize and cross-reference. This requires adjusters to navigate between systems and manually piece together information, making the process time-consuming and prone to errors.
• Manual Data Entry: Adjusters must manually populate information, such as re-entering policyholder details from paper forms into digital systems or manually reviewing physical documents to cross-check claim details, increasing the risk of errors.
• Inefficient Communication: Adjusters often need to switch between multiple platforms to contact policyholders and vendors, leading to missed messages, duplicated efforts, and a lack of cohesive communication history.

These inefficiencies delay claim resolution, hindering adjusters’ ability to provide the attentive service that policyholders expect. 

For insurance providers, the inability to adapt to new business models and integrate with modern technologies becomes a significant barrier to staying competitive.

AI-Powered Tools are the Answer to Effective Claims Handling

The rise of AI-driven technologies is transforming claims management. These advancements bridge the gap between outdated, inefficient legacy systems and the modern, integrated technology needed for efficient claims handling. Here’s a closer look at how these tools modernize the process:

Understanding Disorganized Text

AI understands free-form, unorganized information such as human instructions, notes, process documentation, emails, and any types of documents, leveraging context for accurate interpretation. 

In claims management, this capability enables seamless human-machine interaction (e.g., chatbots), effectively manages incoming and outgoing communications, ensures strict process compliance without the need for hard coded rules, and enhances quality assurance throughout the claims lifecycle.

Similarity Search & Context Awareness

AI can extract and organize information from various sources, such as names, dates, places, numbers, and communications, while identifying patterns and similarities within the text.

In claims management, this capability supports critical processes by streamlining claim intake like the First Notice of Loss (FNOL), facilitating quick classification and prioritization of claims during triage, delivering accurate and comprehensive data for coverage decisions, aiding thorough liability investigations, and ensuring precision in invoice and payment processing—all while reducing manual errors and saving valuable time for adjusters.

Summarization and Analysis Using Natural Language Processing (NLP) 

AI can summarize lengthy documents, such as medical reports and legal documents, by providing comprehensive overviews that highlight key points, uncover inconsistencies, and flag missing data or potential issues.

In claims management, this capability proves invaluable for detecting fraud, assessing damages, evaluating litigation risks, analyzing demand letters, and reviewing police reports, empowering adjusters to make well-informed decisions with greater efficiency.

Decision Support and Automation 

AI analyzes data and can deduce and suggest appropriate next steps based on policies and company regulations. It also integrates with other systems to exchange information, enabling it to autonomously carry out tasks efficiently.

In claims management, this means claims are handled with consistent quality and accuracy, while ensuring all processes follow regulations and company standards. It also helps adjusters make better decisions, provides training support, and enables certain claims to be processed automatically from start to finish, saving time and reducing the need for repetitive manual work.