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The Indian health insurance industry has experienced significant growth over the past few decades. This expansion can be attributed to several factors, including heightened health awareness among the Indian population, the rapid proliferation of private players, and policy reforms introduced by the Indian government. This study aims to develop an effective time series forecasting model for a critical metric: the consumer’s claims settlement status, which influences the Indian health insurance industry. The study examined six distinct time series data sets to forecast consumer claims, including Outstanding Beginning Year, New Claims Registered, Claims Settled, Claims Repudiated, Claims Pending, and Total Claims. The data, collected from various segments of the Indian healthcare insurance sector, spans the period from 2005 to 2023. Secondary data for this study was sourced from the Insurance Regulatory and Development Authority of India (IRDAI) and various insurance reports. ARIMA model parameters were identified and implemented using the R programming language to build the forecasting models. The findings indicate that these predictive models can be leveraged to forecast the future business potential of the health insurance sector in India.

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Predictive Modelling of Health Insurance Claims of Customer—An ARIMA Approach

  • 章节

  • 发表日期：

    2025年

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  • Jolly Masih,
  • Meenu Mathur,
  • Garima Khullar

摘要

The Indian health insurance industry has experienced significant growth over the past few decades. This expansion can be attributed to several factors, including heightened health awareness among the Indian population, the rapid proliferation of private players, and policy reforms introduced by the Indian government. This study aims to develop an effective time series forecasting model for a critical metric: the consumer’s claims settlement status, which influences the Indian health insurance industry. The study examined six distinct time series data sets to forecast consumer claims, including Outstanding Beginning Year, New Claims Registered, Claims Settled, Claims Repudiated, Claims Pending, and Total Claims. The data, collected from various segments of the Indian healthcare insurance sector, spans the period from 2005 to 2023. Secondary data for this study was sourced from the Insurance Regulatory and Development Authority of India (IRDAI) and various insurance reports. ARIMA model parameters were identified and implemented using the R programming language to build the forecasting models. The findings indicate that these predictive models can be leveraged to forecast the future business potential of the health insurance sector in India.