The Indian insurance market has undergone significant changes since its early beginnings to become a more dynamic and inclusive sector. The Indian insurance market has seen and achieved key milestones right from its emergence in 1818 to contemporary times. However, in this journey of evolution what remained constant were the operational challenges. As these operational challenges kept evolving, insurance accessibility started becoming more granular and difficult. But then came AI a much-needed messiah in Insurance which transformed the Indian insurance landscape.
Let’s decode more!
The Modern Challenge
Cut to contemporary times, when digital transformation is increasingly streamlining operations for insurers, there lies a gap between affordability and accessibility. In fact, as of 2020, only about 18% of the eligible population subscribed to pure retail term insurance offerings, with protection penetration at approximately 12%. Over 40 crore individuals in India remain uninsured. This number highlights the persistent health protection gap. This gap is particularly widening as well as evident in the rural areas, where 90% of the rural population lacks insurance coverage and incurs high healthcare expenses. In the same manner, the urban poor are also disadvantaged and often get excluded from accessing the modern Health Insurance market due to the concerning factors of literacy and poverty which limit their access to information and resources.
However, there is a slight change in this scenario, insurers are widely resorting to predictive tools for assessing the affordability of users, while highlighting the pressing need for innovative solutions to enhance insurance accessibility and affordability, especially for the marginalized communities in India.
Here’s where Artificial Intelligence comes into the picture!
Hop on to the next section to decode.
AI- The Messiah in Insurance
The Indian Insurance market has been plagued by the traditional challenges which had largely been manual intensive and filled with tedious tasks, the emergence of AI has resulted to be of much relief.
For instance, AI’s ability to process vast amounts of data surpasses the traditional models. Insurers can now identify risky patterns both at the Micro and Macro levels. This includes analyzing the regional risks at the zip code level, insurers will be able to set up pricing models for specific population groups. One of the key aspects that can make a change in the traditional Indian insurance setup is micro-segmentation.
Insurers can move away from generalizing pricing buckets and AI will be enabling the insurers to offer premiums that will align with the unique risks that individuals and communities pose. With this approach, insurers can ensure fair and affordable coverage for low-income groups and will empower them to access insurance without overpaying for premiums or coverage that might not be needed.
AI is turning out to be a key enabler in the Indian insurance market.
How AI is Bridging the Gap in Indian Insurance?
Ai is transforming the traditional Indian insurance landscape with its robust capabilities.
Here’s a closer look:
Sustainable Micro Insurance
The critical aspects of microinsurance are affordability and sustainability. In this aspect, AI-powered agentic workflows for underwriting automation and granular risk segmentation. AI can be used in product development where its robust capabilities can be used in designing innovative products with tailored coverage, payment plans and limits that meet the customer needs. It can increasingly help in optimizing distribution channels and make insurance products more accessible through online platforms or mobile apps. This way agentic workflows will significantly help in bridging the traditional gaps in the Indian insurance market. Additionally, expanding digital access and simplifying the process of insurance will help drive adoption and also reduce financial vulnerabilities for underserved populations.
Personalization through Real-Time Data
With insurers increasingly adopting real-time behavioural data, there have been new avenues that have opened when it comes to hyper-personalization in insurance. This also includes advancements in telematics, wearable technology, and smart home systems. These devices essentially collect data on driving habits, fitness levels, and property risks that can increasingly be analysed using AI.
One of the techniques that insurers can leverage to enhance personalization is reinforcement learning. It is one of the key AI techniques which allows for dynamic pricing based on evolving behaviours. It is a way through which premiums can reflect the real-time risks, considering the individual and population level changes.
Balancing Profitability and Affordability
One of the key challenges that insurers face is balancing profitability and accountability. With AI, striking a balance between profitability and accountability will be streamlined. AL offers a solution by allowing for granular segmentation of risks and affordability groups. AI also enables insurers to price high-risk customers optimally while also offering competitive premiums to low-risk individuals.
Will AI Prove Helpful in Bridging the Gaps in the Indian Insurance Market- Our Thought
Artificial Intelligence has simplified operations and has significantly taken down the traditional, manual-intensive challenges. With India’s demographics taking a shift and addressing the increased need for insurance adoption and evolving customer preferences, there is an increased need for a more personalized approach. Customer-centric solutions that are offered with the efficiency and accuracy of AI are now preferred. Additionally, digital channels and agentic workflows actively contribute to bridging the gap in the Indian insurance market.
The Way Forward…
The emergence of AI is transforming the insurance landscape and is giving insurers a broader perspective to conduct operations while also streamlining them. AI-powered systems are bringing the traditional and critical challenges of the Indian insurance industry to the forefront and are offering varied ways of dealing with them and also increasingly eliminating the chances of manual errors with automation.
