Natural Language Inference Models for Automated Insurance Claim Adjudication

Abstract

Automated insurance claim adjudication has emerged as a critical challenge in contemporary financial and technological ecosystems, demanding robust methods for assessing the veracity and legitimacy of claims without human intervention. Natural Language Inference (NLI) models, initially deployed for tasks such as textual entailment and question answering, present a compelling opportunity for solving this challenge by systematically interpreting textual information and inferring logical relationships. By capitalizing on the unique capacity of NLI to determine whether a hypothesis is entailed, contradicted, or neutral with respect to a premise, this approach can significantly reduce manual review processes, enhance consistency, and boost overall efficiency. In this work, we examine the theoretical underpinnings and practical implementation of advanced NLI models tailored specifically for insurance claim adjudication. We investigate how model architecture, data encoding, and representation learning can be optimized to address the intricacies of claim documents that often contain specialized, context-dependent terminology and nuanced logical dependencies. To provide a robust foundation, we develop advanced mathematical formulations and inject formal logical reasoning methods to ensure the reliability of automated adjudication decisions. Our experimental findings underscore the feasibility of using NLI-based architectures to automate claim reviews with high accuracy, while also highlighting ongoing challenges. Our ultimate objective is to encourage broader adoption of inference-oriented solutions in the evolving domain of insurance technology.