Generative Artificial Intelligence (AI) tools, including ChatGPT and DALL-E, have demonstrated remarkable problem-solving capabilities – generating text, images and even codes. However, these models require the industry-specific context necessary to power relevant business outcomes, as they are typically trained on generic datasets.
Consider this: A Generative AI model trained on a general language corpus may not be as effective as one contextualized for the intricate insurance terminology, regulations and policy language used within a particular insurance company. Without this contextualization, the model may falter when attempting to accurately identify sources of recovery for subrogation or liability-related claims, which are indispensable for insurance companies seeking to manage and mitigate risks effectively.
The advantages of contextualizing Generative AI for insurance businesses are numerous. Contextualization empowers the models to learn the language, terminology and nuances unique to the insurance industry, amplifying their accuracy and usefulness in solving industry-specific challenges. Imagine a Generative AI model trained on insurance claims data, capable of generating accurate and detailed claims reports, saving time and effort for insurance adjusters.
The applications of contextualized Generative AI in the insurance sector are extensive. Let’s dive into three specific use cases:
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.
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.
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.
While contextualizing Generative AI models, insurance businesses must consider three vital technical considerations:
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.
In conclusion, the importance of contextualization in harnessing the power of Generative AI for business problem-solving cannot be overstated. Contextualized models offer greater accuracy, speed and ease of deployment, becoming an invaluable tool for businesses. As Generative AI technology advances, we can expect the emergence of even more sophisticated models capable of capturing the nuances unique to the insurance industry, leading to unparalleled efficiency and accuracy.
However, embarking on the journey of contextualizing Generative AI models can be a resource-intensive process, demanding significant time, effort and specialized expertise in the industry and AI implementation. Therefore, insurers must collaborate with strategic partners that combine deep industry expertise and capabilities in AI to contextualize Generative AI models. Such partnerships would be critical to leveraging the necessary skills and resources to unlock the full potential of Generative AI.
Contact us to know how WNS can help your business harness the power of Generative AI.
WNS Triange (formerly WNS Research and Analytics practice) powers business growth and innovation for 120+ global companies with Artificial Intelligence (AI), analytics, data and research. Driven by a specialized team of over 4000 analysts, data scientists and domain experts, WNS Triange helps translate data into actionable insights for impactful decision-making. Built on the pillars of consulting (Triange Consult), future-ready platforms (Triange Nxt), and domain and technology (Triange CoE), WNS Triange seamlessly blends strategy, industry-specific nuances, AI and Machine Learning (ML) operations, and intelligent cloud platforms.
Driving a futuristic edge are WNS Triange’s modular cloud-based platforms and solutions leveraging advanced AI and ML to provide end-to-end integration and processing of data to actionable insights. WNS Triange leverages the combined strength of WNS’ domain expertise, co-creation labs, strategic partnerships and outcome-based engagement models.
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29 June 2022
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