In India, several communities face significant challenges in accessing affordable insurance. 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 evident in rural areas, where nearly 90% of the population lacks insurance coverage and has to incur high healthcare expenses. The urban poor are similarly disadvantaged, often excluded from affordable health insurance markets due to factors like illiteracy and poverty, which limit their access to information and resources.

This has changed only slightly, as insurers are adopting predictive tools to assess affordability for users. This highlights the pressing need for innovative solutions to enhance insurance accessibility and affordability for marginalised communities in India.

To understand this better, AIM spoke with Affan Mohammad, Principal Consultant – Insurance at Fractal, India’s first AI unicorn. With his years of experience in the financial services and insurance industry, Affan said that while the industry has always faced several challenges, AI may provide insurers with several solutions for finding innovative ways to balance risk assessment and affordability.

“At the heart of AI-driven insurance pricing is data,” Affan explained. AI’s ability to process vast amounts of data surpasses traditional models. Insurers can now identify risky patterns both at macro and micro levels. For instance, by analysing regional risks at the zip code level, insurers can set up pricing models for specific population groups.

“Micro-segmentation plays a crucial role here,” he added. By moving away from generalised pricing buckets, AI enables insurers to offer premiums that align with the unique risks posed by individuals or communities.

This approach ensures fair and affordable coverage for low-income groups and empowers them to access insurance without overpaying for premiums or coverage they may not need.

The Role of Explainable AI

Transparency is critical, especially when catering to underserved populations. Affan highlighted how explainable AI bridges the gap between insurers and customers. “For end consumers, explainable AI provides clarity on why certain premiums or coverage options are offered or denied,” Affan said.

This transparency builds trust and helps people understand how factors like previous defaults or financial behaviours might influence insurance premiums. It incentivizes people to improve their future decisions and practices to be eligible for better products and pricing in the future.

Moreover, explainable AI prevents insurers from being biased in pricing models by checking for compliance with regulatory standards. While regular audits and diverse training data are important, collaboration with regulators also guarantees that AI models are fair and unbiased.

Personalisation Through Real-Time Data

The integration of real-time behavioural data has opened new avenues for hyper-personalisation in insurance. These include advancements in telematics, wearable technology, and smart home systems. “These devices collect data on driving habits, fitness levels, or property risks, which can be analysed using AI,” Affan said.

Reinforcement learning, a key AI training technique, allows dynamic pricing based on evolving behaviors. It is a way through which premiums can reflect real-time risks, taking into account individual and population level changes.

Affan, however, acknowledged the ethical challenges of using such data. “It’s a delicate balance…Transparency, informed consent, and regular reminders about data usage are vital to maintaining consumer trust.”

Designing Sustainable Micro-Insurance

It is crucial for micro-insurance products to be affordable and sustainable. Affan discussed the role of AI-powered agentic workflows for underwriting automation and granular risk segmentation. “AI can design innovative products with tailored coverage, payment plans, and limits that meet customers’ needs…It can also optimise distribution channels, making insurance products more accessible through online platforms or mobile apps.”

While everyone is trying to figure out how assistants and agents can help their industry, Fractal has already implemented multi-agent systems for the insurance and underwriting part of their offerings.

Onil Chavan, client partner, insurance practice and global capability head at Fractal, earlier discussed with AIM how the company uses these multi-agent systems to reshape underwriting processes and drive efficiency in the insurance domain.

Affan also said that such innovations are especially vital in markets like India, where insurance penetration remains low. “Expanding digital access and simplifying the process of purchasing insurance will drive adoption and reduce financial vulnerabilities for underserved populations,” he added.

Balancing Profitability and Affordability

Insurers often face the challenge of balancing profitability and affordability. AI offers a solution by allowing granular segmentation of risk and affordability groups. “We can simulate various scenarios to find the optimal balance between customer retention and profitability,” Affan further said.

AI also enables insurers to price high-risk customers optimally while offering competitive premiums to low-risk individuals. “Affordability and profitability aren’t mutually exclusive,” he asserted. “With the right strategies, insurers can achieve both.”

By bringing together advanced analytics, explainable AI, and real-time data integration, insurers can enhance transparency, personalisation, and accessibility. “AI-driven innovation is not just about profitability; it’s about building trust and inclusivity in insurance,” Affan concluded.