Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey dataThe National Family Health Survey (NFHS), India data is publicly available data set and can be accessed on request. It can be downloaded upon registration from the Demographic and Health Survey (DHS) website upon registration at The DHS Program - Request Access To Datasets. We have used data from the fourth and fifth round of NFHS, which can be accessed after registration from the link given here for NFHS 4 and NFHS 5 https://dhsprogram.com/data/dataset/India_Standard-DHS_2015.cfm?flag=0 and here https://dhsprogram.com/data/dataset/India_Standard-DHS_2020.cfm?flag=0 respectively. These datasets (HR file) have been used to obtain this combined dataset of a paper entitled "Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data" submitted to BMJ Global Health August 2023.

In the fiscal year of 2024, public sector health insurers across India recorded insurance premiums worth about *** billion Indian rupees. That same year, private sector health insurers saw premiums aggregating to over *** billion rupees. In total, the value of health insurance premiums reached about ************ Indian rupees for the first time.

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Insurance Software Market Size 2025-2029

The insurance software market size is forecast to increase by USD 9.87 billion, at a CAGR of 9.3% between 2024 and 2029.

The market is experiencing significant growth and transformation, driven by increasing government regulations mandating insurance coverage in developing countries and the integration of wearables into customer engagement metrics for life insurance. These trends reflect a growing emphasis on risk management and personalized customer experiences. However, the market also faces challenges, including a tightening regulatory environment for insurance players. Compliance with evolving regulations is essential to maintain market position and mitigate potential penalties. Additionally, the integration of wearables presents opportunities for more accurate risk assessment and personalized pricing, but also raises concerns around data privacy and security.
To capitalize on market opportunities and navigate challenges effectively, insurance providers must stay informed of regulatory changes and invest in robust data security measures. By embracing technology and adapting to regulatory requirements, insurers can enhance their offerings and build stronger relationships with customers.

What will be the Size of the Insurance Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, with dynamic market activities shaping its landscape. Entities reporting and analytics, user experience (UX), regulatory reporting, integration APIs, database management, machine learning (ML), data security, cloud computing, data privacy, sales management, and various other components are increasingly integrated to offer comprehensive solutions. Policy issuance, customer portals, document management, and broker management are seamlessly integrated into the policy lifecycle, enabling efficient and effective operations. Predictive analytics, microservices architecture, and agile development are transforming the industry, allowing insurers to make data-driven decisions and respond quickly to market trends. User interface (UI) and mobile applications are essential for enhancing the customer experience, while API integrations and sales force automation streamline internal processes.

Actuarial modeling, billing systems, quality assurance (QA), commission management, and premium calculation are crucial for accurate risk assessment and pricing. Data analytics, claims management, reporting & analytics, and machine learning (ML) are at the forefront of innovation, enabling insurers to detect fraud, process claims efficiently, and gain valuable insights from vast amounts of data. Data security, cloud computing, and data privacy are paramount in ensuring the protection of sensitive information. The ongoing evolution of the market reflects the industry's commitment to meeting the ever-changing needs of customers and regulatory requirements. The integration of these advanced technologies and processes will continue to reshape the market, offering new opportunities for growth and efficiency.

How is this Insurance Software Industry segmented?

The insurance software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud-based


Type

  Life insurance
  Accident and health insurance
  Property and casualty insurance
  Others


End-user

  Insurance companies
  Agencies
  Brokers


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth due to the adoption of advanced technologies such as predictive analytics, microservices architecture, and artificial intelligence (AI) in policy administration, claims management, and risk management. Customer portals and document management systems facilitate seamless interaction between insurers and policyholders, enhancing the user experience (UX). Policy issuance and renewal management are streamlined through API integrations and agile development, enabling real-time processing. Mobility is a key trend, with insurers developing mobile applications to cater to the growing demand for on-the-go access to insurance services. Data analytics and regulatory reporting are essential components, ensuring compliance with industry regulations and providing valuable insights for strategic decision-making.

Policy lifecycle

Introduction

This Indian English Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of English speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Indian English speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

In a randomised field experiment undertaken across 151 villages in South India, local agents were hired to spread information about a public health insurance programme. The resulting article studies the interaction of incentive pay with intrinsic motivation and social distance. It analyses theoretically as well as empirically the effect of incentive pay when agents have not only pro-social objectives but also preferences over dealing with one social group relative to another. A field experiment conducted across 151 villages in Karnataka, India, in the context of a government-subsidized health insurance scheme aimed at the rural poor. In a random subsample of the villages (the treatment groups), one local woman per village was recruited to spread information about the scheme. These ‘knowledge agents’ were randomly assigned to either a flat-pay or an incentive-pay contract. Under the latter contract, the agents' pay depended on how a random sample of eligible households in their village performed when surveyed and orally presented with a knowledge test about the scheme