Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(152)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

Customer Engagement Strategies For Gen Zs in Insurance

Indian market is a multi-headed Hydra that confounds in more ways than one. Being the world’s largest democracy and the most diverse country has resulted in a level of stratification that most countries would be unable to fathom. The tiered expectations and a shift in customer demographic are pushing insurers to rework the Customer Engagement Strategies For Gen Zs.

Tier 1 customers hold businesses to an extremely high standard, often on par with global companies operating out of mature ecosystems like the UK, USA, et al.

Tier 2 customers on the other hand are more rustic in their ways of seeing but actively seek the kind of novelty and flair that their Tier 1 counterparts crave. This cohort also strikes a fine balance between modernity and tradition when it comes to customer engagement expectations, e.g. would prefer talking to a live agent instead of a bot.

Tier 3 customers continue to operate on a major time lag, i.e. fully digital touchpoints do not work and software can be a catalyst for change only insofar as they remain invisible in the interactions that Tier 2 customers have with businesses.

Use Cases:

Given the democratized access to generative AI technologies, insurers would do well to incorporate them in each and every facet of the customer experience, right from purchase, all the way to fraud detection. That being said, regional differences could be accounted for in the following ways:

Tier 1: Metro cities require a comprehensive customer experience approach that never rests. Highly personalized chatbots that operate on context, slick user interfaces that are built to minimize friction in service, and proactive communication (via reminders, automated calls, etc.) are strategies that insurance providers could start using.

Tier 2: Given the relatively less frenzied environment in Tier 2 cities, it would make more sense to devote a sizable portion of the budget towards a digitally-enabled physical office. Incorporating the usual technologies to extend reach, while also maintaining a team in these geographies would give it that added human touch that Tier 2 residents usually appreciate.

Tier 3:

For Tier 3 cities, technology ought to recede into the background and do all the legwork that humans did earlier. A more committed implementation of predictive analytics would be needed as Tier 3 residents don’t have as much of a digital footprint as their Tier 1 and Tier 2 counterparts do. 

Phygital v. Digital

Ensuring stickiness and retention amongst Tier 1 GenZ customers will require a domineering digital play. Establishing multiple touchpoints across popular and emerging platforms would be a non-negotiable strategy. 

Tier 2 customers on the other hand would do well with a digital play with a slight mix of physical touchpoints which could include a singular office in the arena, primarily for servicing and support activities. Customer engagement would require a localization effort, in terms of language as well as distribution.

Tier 3 GenZ members would require a full-fledged phygital strategy where the role of digital engagement would purely be limited to the realm of convenience, by way of sharing documents, essential information, etc. Establishing reasonably spacious offices, coupled with outdoor advertising would be the only way to be ‘taken seriously’ in such geographies.

Next-gen Engagement Models

Both AdTech and MarTech are evolving at a rapid pace, to the point where the cost of implementing experiential engagement strategies is decreasing with each passing year. Audiences in Tier 1 areas will be more receptive to AR/VR engagement that can allow Insurers to integrate physical locations with a slick, digital experience. 

The current ecosystem could even allow for engagement strategies built on the metaverse. These, however, will need to be restricted to upscale commercial/residential areas for maximum effectiveness.

Tier 2 and Tier 3 geographies, on the other hand, are not yet primed for such innovations. The balance between physical engagement strategies, i.e. having a team on the ground, hosting events, and actively reaching out to younger customers in collegiate environments ought to be in favor of the physical, with digital-only being an enabler.

There can be no one size fits all customer engagement strategies. The only way forward would be to carefully select an engagement mix and deploy it dynamically to get the attention of GenZ customers.

Cancel

Knowledge thats worth delivered in your inbox

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

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot