Page 69 - IMDR Journal 2025

Research Article
For example, someone living in a small town might prefer to India, given its unique market dynamics and rapid adoption
speak with a person rather than use an app. Their concerns, of digital technologies. The study employs convenience
fears, and expectations are very different from someone in a sampling to collect data from policyholders who have
metro city. We need more local research to understand what recently filed health insurance claims. While the findings
Indian customers actually want from these systems and how may have broader implications for other emerging markets,
to meet those needs. the primary focus is on understanding the Indian context.
What About the Long Run? The study does not delve into the technical development of
AI algorithms or legal/regulatory aspects, as these are
We also don’t know enough about how these systems affect beyond its scope. Instead, it prioritizes consumer-centric
customer relationships over time. Sure, they make things insights to guide insurers in improving AI adoption and
faster today. But what happens in the long run? Do people
trust.
feel cared for? Do they stay loyal to the company? Or does
the lack of human connection drive them away? Addressing
this gap is critical to ensuring that AI adoption leads to METHODOLOGY
sustainable growth in the Indian health insurance sector.
Research Design
This study employs a descriptive research design to explore
PROBLEM STATEMENT & OBJECTIVES consumer perceptions of AI-driven claims processing in the
Indian health insurance sector. The primary focus is on
Significance / Rationale
understanding how policyholders perceive the efficiency,
The adoption of AI-driven claims processing in the health trust, fairness, and transparency of AI-driven systems. The
insurance sector has the potential to revolutionize efficiency, study relies on primary data collected through structured
accuracy, and customer satisfaction. However, the success surveys and interviews with health insurance policyholders
of AI in this domain hinges on consumer trust, which is often
in Pune, India. The survey includes hypothetical questions
influenced by perceptions of fairness, transparency, and
designed to assess perceptions of AI-driven claims
bias. This results in distrust when the claims are rejected processing, while interviews provide qualitative insights to
without any explanations. Further the algorithmic bias complement the survey data.
complicate the adoption of AI in India's diverse and digitally
divided population. One needs to address these concerns, The research design is exploratory in nature, aiming to
thereby fostering long-term customer loyalty. identify key factors influencing consumer trust and
satisfaction with AI-driven systems. By combining
Managerial Usefulness
quantitative data from surveys with qualitative insights from
This research provides actionable insights for insurers interviews, the study provides a comprehensive
aiming to enhance AI adoption in the health insurance understanding of consumer perceptions. Additionally,
sector. By understanding consumer perceptions of fairness, secondary data from industry reports, academic papers, and
transparency, and trust, insurers can design AI systems that case studies is used to contextualize the findings and support
align with customer expectations. For instance, the analysis. This mixed-method approach ensures a robust
incorporating explainable AI (XAI) techniques can and holistic examination of the research problem.
demystify AI decision-making processes, making them Sources of Data Collection
more transparent. Additionally, insurers can use this
research to address biases in AI algorithms, ensuring fair Primary Data
treatment for all customers. By balancing automation with The primary data for this study is collected through
human oversight, insurers can improve efficiency while structured surveys and interviews with health insurance
maintaining the empathy and trust that customers value. policyholders in Pune, India. The survey includes questions
This study thus offers a roadmap for insurers to leverage AI on respondents' awareness of AI in claims processing, their
as a tool for both operational efficiency and customer-centric satisfaction with the time taken to process claims, their trust
innovation. in AI-driven decisions, and their perceptions of fairness and
Objectives of the Study transparency. Interviews provide qualitative insights into
consumer experiences, challenges, and suggestions for
1. To examine consumer perceptions of AI-driven claims
improvement. The survey is distributed using online tools
processing in the Indian health insurance sector.
like Google Forms, ensuring wide reach and ease of
2. To identify key factors influencing trust, fairness, and participation.
transparency in AI systems.
Secondary Data
3. To analyze the impact of AI-driven claims processing on
Secondary data is gathered from academic research papers,
customer satisfaction and loyalty.
industry reports, and case studies on AI adoption in the
4. To provide actionable recommendations for insurers to health insurance sector. Sources include journals, reports
improve consumer trust and acceptance of AI technologies. from consulting firms like McKinsey and PwC, and
Scope of the Study publications by leading insurance companies. This data
provides context and supports the analysis of primary
This study focuses on the Indian health insurance sector, findings. For example, secondary data is used to compare the
with an emphasis on consumer perceptions of AI-driven adoption of AI in India with global trends and to identify best
claims processing. The geographical scope is limited to

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