Page 68 - IMDR Journal 2025

Research Article
unusual patterns in claims data, such as inflated medical bills What People Are Saying
or unnecessary procedures. However, the authors also noted Researchers have looked into how people feel about these
that AI systems could sometimes flag legitimate claims as systems, especially when it comes to trust and fairness.
fraudulent, leading to customer dissatisfaction.
Most customers like how fast claims are handled now. They
Study 5 Transparency and Fairness in AI-Driven Claims don’t have to wait weeks or deal with piles of paperwork.
Processing (Desai & Joshi, 2022) But at the same time, many are worried. They wonder: Why
This study examined the role of transparency and fairness in was my claim rejected? Was I treated fairly? Can I talk to a
AI-driven claims processing in the Indian health insurance real person if something feels wrong?
sector. The researchers conducted interviews with These concerns are even stronger in India. Not everyone is
policyholders and found that 60% of respondents felt that AI
familiar with digital tools, and many customers don’t fully
systems were fair in evaluating claims. However, 30%
understand how these systems work.
reported concerns about bias, particularly in claim
rejections. The study emphasized the need for explainable What Needs to Be Done
AI (XAI) techniques to provide clear and understandable If companies want people to trust these tools, they need to:
explanations for AI-driven decisions, thereby improving Explain how decisions are made, in simple language. Make
customer trust. sure the system treats everyone fairly, no matter who they
are. Keep the human connection, so people know someone is
Study 6 AI Chatbots in Indian Health Insurance (Rao &
there to help if needed.
Verma, 2023)
At the end, speed and accuracy are important — but so is
This study analyzed the use of AI chatbots in the Indian
health insurance sector, focusing on their role in claims trust. The best approach is one that combines both: smart
processing. The researchers found that chatbots could systems with a human heart.
handle 80% of routine queries, freeing up human agents to Research Gaps Identified
focus on complex cases. However, some customers Lack of Consumer-Centric Studies on AI-Driven Claims
expressed concerns about the impersonal nature of Processing in India
interactions with chatbots, particularly in cases where
empathy was required. The study suggested that a hybrid Despite the growing body of research on AI in health
model, combining AI with human oversight, could address insurance, there is a notable lack of consumer-centric
these concerns. studies, particularly in the Indian context Most of the
research done on smart systems in insurance focuses on
Study 7 Global Adoption of AI in Health Insurance things like fraud detection, speed, and cost savings. But very
(McKinsey & Company, 2023) few studies look at what really matters to customers how
This global report examined the adoption of AI in health they feel about these systems.
insurance, with a focus on emerging markets like India. The For example, we still don’t fully understand what makes
report found that while AI adoption is growing, many people trust or doubt these systems. We also don’t know
insurers struggle with integrating AI into existing workflows enough about what can be done to make the process clearer
and ensuring regulatory compliance. The report also and easier to understand for customers.
highlighted the potential of AI to improve efficiency and This gap in understanding is especially important in a
reduce costs in the Indian health insurance sector, but country like India, where the insurance world is changing
emphasized the need for greater transparency and consumer fast. Many companies are going digital, but success depends
trust.
on more than just technology it depends on whether people
Comparative Analysis of Traditional vs. AI-Driven trust that technology.
Claims Processing
What’s Missing: Clarity and Communication
The empirical studies reviewed above highlight the One big problem is that many of these systems work like a
significant advantages of AI-driven claims processing over “black box.” That means decisions are made, but customers
traditional methods. Smart systems can process insurance don’t know how or why. If a health insurance claim is denied
claims much faster than people can. They’re usually more and there’s no clear explanation, it’s only natural for
accurate too, and they’re really good at spotting fake claims.
But that doesn’t mean everything is perfect. someone to feel confused or even cheated.
Some researchers have tried to fix this by using tools that
These tools can sometimes feel cold or distant. People often
make decisions easier to explain, but not much of this work
don’t understand how they make decisions and that can has been done in the Indian context. We still don’t know how
make them uncomfortable. In contrast, older, manual to make these tools more understandable and reassuring for
methods might be slower and less precise, but they feel more Indian policyholders.
personal. When a human handles your claim, you can ask
questions and feel heard and that builds trust. One Size Doesn’t Fit All
So now, insurance companies face a tough question Most studies on this topic come from countries like the U.S.
or the U.K., but India is different. Culture, education levels,
How do you keep the speed and accuracy of these smart and how comfortable people are with digital tools can vary a
systems, while still making customers feel respected, lot — especially between cities and villages.
informed, and understood?

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