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4 Key Takeaways from India Insurance Summit & Awards 2020

The India Insurance Summit & Awards 2020, themed around technology and innovations in Insurance concluded on March 13th in Mumbai. The event witnessed enthusiastic participation from corporates like Future Generali India Life Insurance, ICICI Lombard, Aditya Birla Sun Life Insurance, Pramerica Life and many more. The stalwarts from the Insurance industry addressed the tech-powered revolution that is soon to happen with Digital 2.0. Here are 4 key takeaways from IISA that highlight the future of Insurance and InsurTech.

1. Digital 2.0 is on rise

Accenture’s research report on the post-digital era reveals that 94% of businesses have accelerated their digital transformation over the past three years. While the era of Digital 1.0 was focused on the mobile, simplified design and a wider range of applications, Digital 2.0 extends the ecosystem into the next-gen interface which relies on anywhere, anytime and any platform mindset.

The traditional insurance distribution channels have already received a digital facelift; with Digital 2.0, they tend to become more consumer-focused and experience-driven. Insurers are empowering distributors to deliver next-gen experiences to customers and deliver products & services for Micro-Moments

[Related: How technology is transforming Insurance distribution channels]

2. Millennials are characterized by Micro-Moments

Micro-Moment is an intent-rich moment when a person turns to a device to act on a need — to know, go, do, or buy” (Google).

An average consumer experiences hundreds of micro-moments throughout the day. More than 91% of smartphone users use mobile phones for inspiration in the middle of a task. People are becoming more research-obsessed and almost every decision made online is informed. For instance, 51% of digital consumers have purchased from a company other than their intended brand, solely based on the information they find online. Moreover, 62% of people are more likely to take an action (like purchase decision) right away even in the middle of some other task.

Earlier, customers used to view the lowest priced product as their best value for money option. Now, the customer’s ability to research is leading to higher-priced products being bought because of the greater perceived value of the product.

As a notion, Insurance is not bought; it’s sold. Thus, micro-moments present immense opportunities to engage with the customer during their buying journey. By leveraging the right points of interaction, Insurers can propose relevant and personalized insights to win customers.

[Related: Millennials and Insurance beyond convenience]

3. Online is best for small-ticket insurance 

Small-ticket insurance (or bite-size cover) focuses on the specific needs of consumers. These are characterized by low premium, low cover and hence lower profit margins. Thus, offline distribution, which involves agents and brokers isn’t feasible. Online channels with emerging API-based distribution and marketplaces are best for distributing small-ticket insurance products. In India, companies like Toffee Insurance, MobiKwik and Digit Insurance provide bite-size insurance. 

Within life insurance, term plans are sold the most online. Insurers have observed that online customers buy more and stay longer with the brand as compared to offline customers. In general, online products are more compelling. The key is — small market, great margins and greater profitability.

Moreover, small-ticket insurance delivers two-fold benefits. Consumers, who haven’t bought an insurance product before, need not pay lengthy premiums (also beneficial to Insurers for customer acquisition); while Insurers find it easier to predict customer behaviour online, allowing them to underwrite risks more accurately.

4. Technology will enhance post-sale moments of truth

Insurers have already started to utilize technologies like NLP to build self-service policy renewal/inquiry portals, AI for zero-touch integrated claims, to name some. The behaviour of the same customer on different channels (like Twitter, Instagram, LinkedIn etc.) is unique. Carriers have to map and understand these behaviours to create better-individualized journeys. Distributor journeys also play a crucial role in analysing post-sale moments of truth. Insights from distributor journey can help Insurers modify/add products into the chain based on buyers’ experiences.

Technology is also helping Insurers participate in a connected information ecosystem. Data from geo-tagging of accidents can be shared with law enforcement to understand areas prone to accidents, underlying causes and even catching criminals through facial recognition technology. For instance, Staqu Technologies, a Gurugram-based AI startup, is providing facial recognition systems to many state government police departments.

Wrapping up

Although 94% of urban and 24% of the Indian rural populace use the internet, Insurers still rely heavily on offline third-party insurance sold by agents (e.g. third party motor insurance for the rural market).

Even though online is cheaper than offline, customers prefer offline as it has more accountability. What drives offline to online is understanding that every customer is unique with unique needs and unique propositions. The truth of the matter is — when things fail, online becomes harder for customer acquisition. AI and Automation has allowed for significant cost reduction and process efficiency gains across the value chain for carriers. However, AI should be used strategically to augment processes that cannot be entirely automated so as to not fully eliminate the human in the loop, in order to better assist customers (eg: speaking to an actual person for resolving complex issues.)

Mantra Labs was a proud customer experience partner at India Insurance Summit & Awards 2020. During the event, Mantra unveiled the Internet of Intelligent Experiences (IOIX) illustrating the extremes to which technology can create sensory disruption in customer experiences!

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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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