Journal of Artificial Intelligence and Big Data Disciplines

Workers compensation claim processing is extraor￾dinarily inefficient and causes claimants and employers to suffer. In a typical Australian workers compensation insurer, the Claim Establishment Process can require up to 28 manual steps performed by claim adjuster. Claims departments follow strict business rules that stipulate if a claimant is eligible for certain claim benefits as well as the appropriate medical certificates, certificates of capacity, statutory reserves and claim flags to allocate to the claimant. These are often automated or semi-automated in complexity, however in practice, require considerable agentic decision-making to complete the processing. Claim adjusters perform these decisions, which can be expensive and introduce wait times that delay claimants and employers from receiving their benefits. This feasibility study presents a deep learning framework designed to automate the workers compensation claim process using generative AI. A proof of concept application, entitled ClaimGPT, was implemented using the OpenAI API and Salesforce Einstein GPT, providing agentic AI capabilities for claim generation and decision-making. These capabilities were combined with natural language processing models for claim textual data and image generation for claim documentation. The framework was implemented in two existing workers compensation insurers, focusing on the Claim Establish￾ment and Claim Decision Process. Both implementations were successful in accelerating the claim processing, while reducing the size of the claim reserves allocated to each claim established. The image generation models for claim documentation synthesised novel claim scenes and demonstrated claim simulation capability on a specific insurance use case.