Leveraging AI for Data Mapping in Life Insurance System Conversions

Anshul Kaushik

Abstract: Data conversion in the life insurance industry is a complex and time-intensive process due to the long history of legacy systems, product variations, and nuanced regulatory requirements. Traditional data mapping methods require significant manual effort, making large-scale system conversions both risky and inefficient. This paper presents a novel approach that leverages artificial intelligence (AI), specifically vector search powered by fine-tuned language models, to streamline the data mapping process. By developing a custom AI model tailored to life insurance terminology and workflows, and integrating vector embeddings for semantic field matching, we achieved a 70% reduction in manual mapping time. The results indicate strong potential for AI to accelerate digital transformation in insurance by simplifying one of its most resource-intensive components.

Keywords: Life Insurance, Data Mapping, System Conversion, Artificial Intelligence, Vector Search, BERT, Word2Vec, GloVe, Semantic Matching, Data Migration, Insurance Technology, Legacy Systems, Prompt Engineering, Fine-tuned Language Models

How to Cite?: Anshul Kaushik, "Leveraging AI for Data Mapping in Life Insurance System Conversions", Volume 14 Issue 7, July 2025, International Journal of Science and Research (IJSR), Pages: 1072-1074, https://www.ijsr.net/getabstract.php?paperid=SR25715202539, DOI: https://dx.doi.org/10.21275/SR25715202539