Harnessing GenAI for Claims Processing: Best Practices, Challenges, and the Future

Lee Elliston, Chief Operating Officer for Claims, recently spoke at the Intelligent Insurer Conference on how to harness GenAI to streamline claims processing. The panelists discussed best practices, challenges, and future trends in integrating GenAI into the claim’s lifecycle.  Read some outtakes from Lee below:  

Extracting Reliable Information from Unstructured Claims Data

Ensuring that GenAI extracts accurate and reliable information from complex, unstructured claims documents require a strong foundation in data governance. Implementing robust standards, controls, and processes is critical to maintain quality control and to establish mapping that turns unstructured data into structured data that is available near real-time. At Aspen, we have established data governance, controls, and a focus on evolving our data models, enabling remediation of data and the redefining of data structure and standards.  This targeted approach allows insurers to extract only the most relevant information, converting it into structured data that is easier to analyze and utilize in the claims process. 

Overcoming Challenges of Legacy Systems

Legacy systems often pose significant barriers to integrating GenAI seamlessly. To modernize infrastructure or work around these limitations, insurers can: 

• Implement a centralized data source and a data model that serves as a single source of truth, ensuring that controlled and consistent data can drive and not disrupt the claims process.
• Consider the roadmap of your systems and processes and establish an opportunity to leverage the legacy systems as a foundation (not a barrier) that integrates with a front-end platform, which is where the simplified processes and power of Gen AI can be realized.
• Unravel multiple data sources and inconsistencies in data structure to enable more effective AI integration.
• By restructuring data and simplifying key processes, insurers can create a more efficient and scalable AI-powered claims system. 

Streamlining the Claims Process: Where AI Automation Delivers Value

GenAI offers numerous data processing and data insight opportunities throughout the claim’s lifecycle. Key areas for AI-driven efficiency gains include: 

• Triage & First Notice of Loss (FNOL): Automating initial claim assessments to segment and validate cases efficiently and consistently.
• Compliance Checks: Ensuring regulatory requirements are met with automated controls, validation processes, and alerts on data points and characteristics.
• Intelligent Assistance: Data points and rules can determine the validity of the claim and recommend actions and risks associated with defined logic, data, and rules, to support assessment of coverage and quantum for human decision making. 

Overall, the integration of AI into (re)insurers’ claims processes present numerous opportunities to enhance efficiency, as well as the potential to upskill and empower employees to focus on product and customer focused engagement, portfolio management, and loss prevention.  

It is essential to maintain accountability and transparency in our methods to ensure fairness and accuracy, rushing to fully automated processing in all cases is not viable or equitable, and the approach to the implementation of AI tooling needs to be informed by viable use cases, which assess the impact on regulation, customers, and the workforce.  

There also needs to be consideration of the dependency on controls and processes that are required to stabilize and scale your use of AI as the industry progressively incorporates AI and data analytics into claims practices. Those who adopt these technologies ethically and efficiently, whilst managing data governance and unstructured data points, will significantly improve operational productivity but doing so in a robust and realistic way, starting small (in scope) and delivering big (in benefits) is the way to harness the power of AI.   

At Aspen, we are progressing with small concepts and low code platforms whilst refining data standards by lines of business, to develop and embed the Intelligent Assistant in the claims process, via our Aspen Data Lab and an approved change project in support of Reinsurance optimization.