Traditional AI excels at analyzing vast datasets, including historical policy and claims data, offering a comprehensive evaluation of underwriting and claims risks. Meanwhile, using large language models, GenAI can be trained to perform tasks like summarizing notes, writing emails and providing insightful guidance.
This combination streamlines insurance underwriting and claims processes, enabling insurers to make better decisions about risk, increasing policy pricing accuracy and enhancing claims outcomes.
Generative AI has the potential to significantly singapore whatsapp number data reduce insurance claim costs and duration by performing time-consuming tasks and guiding adjusters toward optimal actions. It can analyze a vast amount of data to provide actionable recommendations.
Imagine an insurer handling a worker’s compensation claim for an injured employee. Traditionally, the process would involve reviewing medical records, consulting healthcare providers and manually assessing the worker’s condition to determine the appropriate course of action. This can lead to delays, prolonged worker absence, and higher claims costs.
Leveraging traditional and generative AI, the adjuster inputs data such as medical reports, diagnostic test results, adjusters’ notes and job requirements. Traditional AI algorithms can leverage available data, analyzing past similar cases and monitoring the recovery of an injured worker continuously. Based on this analysis, GenAI can provide appropriate recommendations for the best next actions, like.