Document Processing
Prudential needed to handle a growing volume of medical insurance claims more efficiently and accurately, while reducing manual data-entry errors and shortening approval and payout times for customers.
Reported impact
Prudential reported faster and more frictionless claims handling, improved claims-decision accuracy, higher automation in reviews and assessments, the ability to handle greater claim volume and velocity, and better customer experience through quicker approvals and pay-outs.
Prudential plc is deploying Google Cloud's MedLM, a healthcare-tuned family of generative AI language models, to support medical insurance claims processing in Singapore and Malaysia. In this implementation, claim-related documents submitted with health insurance claims—including diagnostic reports, prescriptions, and invoices—are passed to MedLM for analysis, summarization, information extraction, and medical coding assistance. The AI output is then used to support claims review and assessment rather than replace it outright: Prudential is comparing MedLM's analysis and advice against outcomes from its current approval processes over a 3-4 month period, with human assessors kept in the loop at critical decision stages. The design is intended to improve decision accuracy, increase automation, and let claims teams process higher claim volumes with faster turnaround and less friction for policyholders.
Medical claim submissions and attached documents such as diagnostic reports, prescriptions, and invoices are provided to MedLM on Google Cloud. MedLM analyzes and summarizes the materials, extracts relevant details, and produces coding and decision-support advice. Prudential then compares that output with decisions from its existing approval process, while human claims assessors review critical stages before approvals and payouts.
"MedLM supports human decision-making with its ability to extract relevant information and code it accurately for claims, helping to reduce the potential for errors caused by manual data entry, so claims can be processed faster and more accurately."
"Prudential will apply MedLM to select medical insurance claims made in Singapore and Malaysia over a period of 3-4 months, then compare the AI's analysis and advice to actual decisions using current approvals processes during this time."
"Prudential will use Google's MedLM family of language models to simplify and summarise claim-related documents, including medical reports and invoices, for quicker approvals and pay-outs"
"Starting in Singapore and Malaysia, Prudential will use MedLM to analyse documents submitted alongside health insurance claims, such as diagnostic reports, prescriptions and invoices."
"maintaining a 'human in the loop' at critical stages of the decision-making process"
"proof-of-concept tests by Prudential ... showed its use of MedLM doubled the automation rate of claim reviews and assessments, in addition to improving the accuracy of claims decisions"
Review how this reference was scored and trace each normalized claim back to its documented source.
Scoring methodology
Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.
Traceability
Field coverage shows which facts each source supports. Dates are shown only when documented by the source.
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