Automating Insurance Benefit Interpretation: A Domain-Intelligence Pipeline for Document Extraction, Benefit Classification, and Plan Recommendation

Authors

  • Susil Kumar Sahu

Keywords:

insurance benefit interpretation, document intelligence, named entity recognition, benefit classification, semantic normalization, plan recommendation, explainable artificial intelligence, human-in-the-loop validation, auditability

Abstract

Large volumes of benefit documentation are processed by insurance organizations, encompassing things, for example, deductibles, copayments, coinsurance, exclusions, service limits and the pharmacy benefits, and a ton of the information is yet to be comprehended consistently before plans can be contrasted or set up within enterprise sales systems. In this systematic review, we compile 28 sources in document image analysis, named entity recognition, pre-trained language models, text categorization, weak supervision, recommender systems and explainable AI to formulate a domain-intelligence pipeline for benefit interpretation. The pipeline includes document ingestion, content extraction, insurance domain entity recognition, benefit classification, semantic normalization, validation, and plan recommendations. A domain taxonomy enables the mapping of carrier terminology to standardized benefit categories, validation rules are applied to disambiguate incomplete or ambiguous values and send them for human review, and the normalized representation is matched to a versioned product catalog to generate ranked candidate plans, including a confidence score, and a sourced linked explanation. The evidence reviewed shows that a form understanding task can be enhanced by a layout-aware pre-training that can boost entity-level F1 score from 78.95% to 84.20%, domain-adapted language models outperform hand-curated training sets by 12.24 points on a biomedical question answering task, and that weak supervision can generate data sets that are as good as the hand-curated training sets within 3.60 points. Provenance and extracted values, normalized representations, product versions and recommended histories are preserved for auditability. The synthesis confirms that document intelligence and domain-specific normalization can help minimize manual interpretation and ensure consistency and scalability in regulated insurance sales operations.

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Published

31.12.2022

How to Cite

Susil Kumar Sahu. (2022). Automating Insurance Benefit Interpretation: A Domain-Intelligence Pipeline for Document Extraction, Benefit Classification, and Plan Recommendation. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 782 –. Retrieved from https://mail.ijisae.org/index.php/IJISAE/article/view/8569

Issue

Vol. 10 No. 4 (2022)

Section

Research Article

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