Page 66 - IMDR Journal 2025

Research Article
CONSUMER PERCEPTION OF AI-DRIVEN CLAIMS
PROCESSING IN HEALTH INSURANCE EFFICIENCY,
TRUST, AND TRANSPARENCY

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Gaurav Ware , Abhishek Powar , Ashwini Shinde 1

ABSTRACT
This research investigates consumer perceptions of AI-driven claims processing in the Indian health insurance sector,
focusing on efficiency, trust, fairness, and transparency. With insurers increasingly adopting technologies like deep learning
and machine learning to streamline operations and detect fraud, AI has emerged as a transformative force. While these systems
reduce processing time and enhance accuracy, the study reveals significant consumer concerns related to trust and
transparency. Using a descriptive and exploratory research design, data was collected from 150 policyholders in Pune via
structured surveys and interviews. Results show that 92.6% of respondents view AI systems as more efficient than traditional
methods. However, only 45.1% trust AI decisions, citing issues such as unexplained claim rejections, algorithmic bias, and
impersonal interactions. In conclusion, while AI offers significant benefits, its success depends on consumer acceptance,
ethical deployment, and the ability to address user concerns in a digitally diverse environment like India.
KEYWORDS Algorithmic Bias, Consumer Trust, Customer Perception, Explainable AI (XAI), Fraud Detection, Hybrid
Human-AI Models, Insurance Technology, Transparency in AI.

INTRODUCTION In the old days, people had to go through each claim by hand
or use basic computer rules. It was slow, and things often
Conceptual Framework
slipped through the cracks.
Deep Learning in Health Insurance Claims Processing
Now, smarter systems can help. These tools can look at past
Deep learning, a subset of artificial intelligence (AI), has claims, spot odd patterns, and raise a red flag when
emerged as a transformative technology in the insurance something doesn’t seem right. If a claim looks suspicious, it
sector, particularly in claims processing. Deep learning is a gets checked more carefully. This helps stop fraud early and
type of advanced computer program that learns from large saves money.
amounts of data. In health insurance, it can help make But that’s not all these smart systems can also handle the
decisions about claims by studying past records and spotting boring, repetitive parts of the job.
unusual patterns.
For example, they can fill in forms, check documents, and
For example, when someone files a claim, deep learning can
quickly go through old medical records, doctor reports, or even review medical records. That way, real people can
handwritten notes to check if the claim seems valid. It can focus on the more serious or complicated claims.
also guess whether there’s a chance of fraud based on how Let’s say someone files a claim for a surgery. The system can
similar cases looked in the past. read through the medical report, check if the surgery was
really needed, and even estimate how much it should cost. It
This means the process can happen much faster, with fewer might also guess how long the person will take to recover all
people needed to check each file. It also reduces mistakes in seconds.
that can happen when everything is done by hand.
This makes everything move faster and reduces mistakes.
One major way this technology helps is by looking at
And when mistakes go down, customers are happier too.
medical images like X-rays or MRI scans. It can spot
problems or confirm if a treatment really was needed. It can How These Systems Help
also read written records and descriptions to find anything Learning from past experience: The system “remembers”
that doesn’t match up like if the treatment mentioned doesn’t what fake claims looked like before and uses that knowledge
fit the patient’s history. to catch new ones. Looking at complex stuff like medical
By doing all this automatically, deep learning helps images: It can even spot things like a broken bone or a tumor
insurance companies work faster, make better decisions, and on a scan to see if the treatment makes sense.
catch fraud all while making fewer errors. Catching anything that feels off: If a claim seems too
AI’s Role in Fraud Detection, Efficiency & Automation expensive or doesn’t match the usual pattern, the system
flags it for a closer look.
Fake insurance claims are a real headache for companies.
They cost a lot of money and take up a lot of time to deal Together, these tools make the whole process smoother -
with. fewer delays, fewer errors, and quicker decisions. That
Corresponding author: gauravware03@gmail.com
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Institute of Management Development and Research, Pune
Cite this Paper :
Gaurav, W., Abhishek, P., Ashwini, S., (2025)
Consumer Perception of AI-Driven Claims Processing in Health Insurance: Efficiency, Trust, and Transparency, JMDR
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