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Incipient Insurance: Attitudinal Variations amongst Gen Z in India

There is no getting around the fact that India, despite being one of the world’s leading economies has an abysmally low level of penetration when it comes to Insurance.

As a new cohort makes its way to working age and begins to confront the many dilemmas of adulthood, Insurance seems to have taken center stage. A looming pandemic, coupled with the younger generation being witness to the ill effects of rapid urbanization and sedentary lifestyles has highlighted the importance of insurance to India’s GenZ population.

Tiered Expectations

Urban India hosts about 30% of the Indian population, with the remaining 70% being distributed amongst Tier 2/Tier 3 cities and rural areas. In the absence of definitive data regarding GenZ’s outlook towards Insurance, we shall rely on the prevailing attitudes demonstrated by millennials (who are astoundingly close to GenZ when it comes to outlook and behavior).

An online study conducted by Policybazaar revealed that respondents from Tier 2 and Tier 3 cities were far more likely to renew their health and term insurance when compared to their Tier 1 counterparts (89% versus 77%). 

A large part of this could be attributed to Tier 2/Tier 3 cities being more grounded in familial values, and higher incidences of diseased folk not having access to advanced medical care in times of distress. Furthermore, Tier 2/Tier 3 cities are less likely to feature more avenues of distractions thereby inculcating a more conservative attitude amongst the younger folks in these places, particularly GenZ. 

This attitude has a direct bearing on the kind of services that GenZ customers from smaller towns expect. Since they are not as informed, they tend to seek more information and niche insurance plans that are uniquely suited to their needs. Agents who can empathize with them are also a welcome addition to it. 

As for Tier 1 residents, those who come from relatively affluent backgrounds are less likely to worry about insurance as they have a solid safety net to fall back on. Consequently, expectations have less to do with the variety and depth of insurance plans, and more to do with slick, delightful user interfaces that are on par with the other consumer-facing apps that they are used to.

Several respondents, across both Tier 1 and Tier 2/Tier 3 cities who were hospitalized experienced the distress of not having a proper insurance plan (or a plan with limited coverage) and were jolted into seeking a comprehensive insurance plan. The collective sentiment is that health coverage ought to hover anywhere between ₹15 – ₹20 Lakhs to ensure that medical expenses do not end up denting one’s savings.

Despite the ongoing economic slump, GenZ has woken up to the perils of putting the horse before the cart and is more likely to prioritize their health over almost everything else. The insurance market could very well experience a period where demand is relatively inelastic as Insurance becomes a non-negotiable for many young Indians.

InsurTech firms and a redefined Insurance distribution playbook only mean that the age-old model of deployed agents and brokers is going to be upended. GenZ, being a digitally savvy and precocious lot is more likely to undertake extensive research and seek out honest advisors before purchasing an insurance product.

Insurance, Disrupted

Technology has finally caught up to the insurance industry and is working its way toward disrupting it at a record pace. Improved connectivity and radically improved customer service in adjacent industries have raised the bar for satisfying GenZ. This is the primary factor that is driving the expectations and attitudes of GenZ when it comes to Insurance.

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

  • Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
  • Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

  • Can AI recognize its own mistakes?
  • What would “self-awareness” look like in AI without consciousness?
  • Are there techniques to make AI second-guess itself?
  • Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live 

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

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