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The Adoption of Chatbots across Insurance

The global chatbot market is expected to reach USD$ 1.25B by 2025, and generate roughly $8B savings globally by 2022 itself. With chatbots disrupting a wide variety of industries already, the technology is becoming more popular in a variety of business use cases – especially within the insurance sector.

Chatbots are becoming more advanced

Chatbots are a natural extension of the push for self-service capabilities. Yet in spite of its growing popularity, according to a recent white paper published by Cognizant Research, almost 60% of insurers surveyed worldwide are yet to implement a chatbot. According to Cognizant’s research (validated with our own internal findings), bot capability is derived from the maturity of the bot; either basic, moderate or advanced.

What makes chatbots effective

Based on this spectrum, ‘basic’ implies that a bot is mostly rules-based and can follow only simple instructions often deferring to a human, whereas those bots that are closest to a true human-like conversation, are classified as ‘advanced’ in terms of their capability. The maturity level of the bot is determined by their performance and their ability to Communicate, Comprehend and Collaborate with the user, providing utility across the value chain. These three C’s are key factors in distinguishing an effective bot from an unsatisfactory one.

Of insurers that have utilized chatbots in their operations, a majority 68% utilise only a basic form of the technology. While insurers have already benefited by saving costs and reducing customer servicing time using them, there is still significant opportunity for the uptake of more capable, reliable and intelligent bots to be deployed across the insurance value chain.

Europe has the highest volume of basic maturity chat bots among insurers at 60%. Asia along with MEA promises the most potential in terms of size and CAGR to adopt chat bot technologies over the next five years. North America is still the largest consumer of ‘advanced’ bots in insurance compared to all other regions.

Chatbots – leading CONSUMER AI APP for the next 5 years

Limitations to overcome

Insurers need to focus on these limitations faced by chatbots to realize their business imperative.

  • Need of human-centric interface: Most of the time, interactions with chatbot are still robotic, providing the end-user with a frustrating non-human centric experience.
  • Inability to contextualize conversations: Bots are programmed to follow a specific sequence or an algorithm, causing an inability to understand the nuances of human language – that results in an unfulfilling and an inauthentic experience.
  • Scalability issues: Developers need to anticipate and program the bot according to the exponential rise in the amount of traffic that the bot might handle.
  • Privacy and data protection: Data is both an asset and a liability. Since customers often give away personal data while conversing with a chatbot, insurers need to prioritise their privacy and data protection regulations for that region.

Opportunity Landscape for AI-enabled Chatbots

Chatbots can be leveraged for both simple and complex insurance processes in order to create definitive business value. Distinct successes have been noted in areas of:

AI Chatbot in Insurance Report

AI in Insurance will value at $36B by 2026. Chatbots will occupy 40% of overall deployment, predominantly within customer service roles.
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Insurtechs will lead the pack

Among other reasons for the large-scale implementation of chatbots, is that insurtechs predominantly target the tech-savvy millennial and Gen Z population who are more open to change and disruptive innovation. Positive customer experiences are directly proportional to twice the referrals, thereby expanding business scope by breaking traditional customer-interaction limitations.

Reimagining Insurance with Chatbots

The insurance industry has reached a revolutionary crossroad that mandates insurers become digitally agile. Over the next few years, chatbots are set to bring about a massive change to the industry and Insurtechs are leading the way in bringing AI-powered chatbots to the insured customer.

  • Lemonade: The NY-based insurtech relies on its app-based chatbots, backed by AI, that can craft personalized insurance policies & quotes for customers, and respond swiftly to a variety of customer queries and process claims.
  • Next Insurance: The insurance provider launched a chatbot via Facebook Messenger through which small businesses can obtain quotes and buy insurance.
  • Trōv: The company has integrated a chatbot into its mobile app that handles customer queries and claims by seamlessly gathering incident related information from the customer.
  • LeO: The insurer recently launched a chatbot which helps schedule calls and meetings, collect leads and answer customer questions automatically – allowing agents to focus on other tasks.
  • Religare: It’s one of the top health insurers in India and a part of major financial service conglomerate. The company has integrated an AI empowered insurance chatbot, that focuses on learning from actual human interactions over a question-answer driven format to build a more intuitive chat based sales funnel.

There is a direct relation between the positive Customer Experience provided by the chatbots and the hike in the revenues. Almost one-third of the insurance business is expected to be generated via digital channels in the next 5 years. The companies that leverage AI-driven customer data for chatbots shall flourish far into the future.

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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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