Generative AI in Insurance: The Need for Contextualization

Three Compelling Use Cases of Contextualized Generative AI in Insurance

The applications of contextualized Generative AI in the insurance sector are extensive. Let’s dive into three specific use cases:

1. Medical Summarization

   Medical summarization is a critical and time-consuming task, demanding accuracy and precision. In this realm, contextualized Generative AI models can be vital within the healthcare and insurance industries. Picture this: Insurance professionals are inundated with medical document summaries and burdened with the arduous task of coding and categorizing them into sections such as diagnoses, costs and treatment plans. Currently, this process is mired in manual labor, extending the timeline of claims processing.

   Now envision the transformative power of Generative AI models seamlessly automating the medical summarization process, reducing the time required to categorize and condense medical documents. However, to achieve accurate summaries with high precision, these models need domain-focused contextual training, leveraging similar medical reports and other relevant documentation. This training equips the AI model to understand medical languages, diagnoses, medications, abbreviations and the intricacies of Current Procedural Terminology (CPT) / International Classification of Diseases (ICD) codes. The result? Contextualized models capable of crafting concise, yet accurate summaries that contain all the relevant information necessary for effective downstream decision-making.

2. Subrogation

   Subrogation unfurls as a legal process that enables insurers to recover the costs of a claim by pursuing legal action against a third-party accountable for the loss or damage. Often, the liable party carries insurance from another company altogether. However, identifying potential subrogation cases and preparing subrogation documentation demand exhaustive attention to detail and an astute grasp of legal expertise, leading to tedious and retrospective claims.

   Now re-imagine this process: A contextualized Generative AI model, steeped in legal documentation, analyzes the First Notification of Loss (FNOL) and other claims-related documentation, generating legally binding and accurate subrogation documents. This diminishes the waiting time for legal experts, ensuring claims are submitted promptly and efficiently. The fusion of Generative AI and traditional predictive analytics helps in determining the recoverability of subrogation while highlighting the key criteria to consider, suggesting the next-best action.

3. Liability

   When it comes to insurance claims arising from accidents, determining liability is a pivotal aspect. Liability refers to the degree of responsibility held by each party involved in the incident, a determination strictly governed by case laws. Currently, insurance experts rely on the narratives of claimants and third-parties to assess the share of liability based on legal precedents.

   However, a contextualized Generative AI model can revolutionize this process by swiftly delving into the claimant's perspective and generating an initial view of liability. As the model assimilates the narrative of the third-party, it dynamically updates its perspective, drawing upon the wealth of case laws it was trained on to make a liability determination almost instantaneously. Although the model's liability assessment need not be 100 percent accurate, establishing a split of liability during the FNOL can significantly streamline the claims process and reduce processing time.

   In essence, implementing Generative AI models for liability determination in insurance claims could pave the way for a more efficient and accurate process. It could save time and resources for the insurance company and the claimants, facilitating a more satisfactory resolution of claims.

Availability of industry-specific datasets: The availability and quality of industry-specific datasets have long been a challenge for Machine Learning (ML) models. To ensure the effectiveness of Generative AI tools, it is imperative to train them using datasets with good lineage and quality, allowing the models to assimilate the business context effectively.

Model architecture and training methodology: Generative AI models offer a range of Application Programming Interfaces (API) and other interfaces for training. Choosing the appropriate architecture and training methodology can significantly impact the model’s accuracy, efficiency and cost-effectiveness.

Model maintenance: Continuous updates and maintenance are essential to uphold relevance and prevent model drift. Businesses must proactively feed the model with new data to keep it up-to-date, ensuring its continued accuracy and effectiveness.