When I look back at how rapidly InsurTech is evolving over the years, it makes me wonder what will happen in the next few years. Although we cannot be sure of the technological advancements we can predict some of them for the next year.

Could 2019 be the year of some more interesting digital transformation in InsurTech? Here are some of our predictions:

1. Underwriters will gain knowledge with AI:

Underwriting is one of the essential components of the insurance business and no matter what no machine can replace underwriters. But, we can expect that by 2019 underwriters will get a lot of assistance for their work procedures through AI and RPA. It will help them manage high volumes of data, parse and validate evidence faster and more cleanly through automation. Reports suggest that insurers spend most of their time doing mundane tasks such as data entry instead of performing core operations. The relevance of AI in 2019 will be more impactful for underwriters as it will free them from those additional time-consuming tasks.

2. Blockchain for centralising medical data:

Blockchain provides a central platform for digital medical records that underwriters can access securely. In the next year with the evolution of the regulatory environment around blockchain transaction and the streamlining and automation of underwriting processes, the blockchain will play a vital role in this industry.

3. Top-notch security:

The one thing that all sectors struggle with when they adopt the digital platform is cybersecurity. The networks are always vulnerable to cybercriminals and could be exploited at any point. Insurance firms also face the same issues with legacy systems that are often hard to update so 2019 will also see some advancement in this field and will focus on making platforms much secure, reducing risk across the enterprise.

4. Improved customer experience:

Customers are becoming digitally savvy by each passing day, and they expect a compelling user experience for every digital transaction they make. The insurers need to ensure that they provide their customers as well as the distribution partners a well crafted digital platform that is compatible with new devices while also offering them a personalised experience. This step will also be instrumental in targeting the millennial generation who generally do not take an interest in insurance policies because of its cumbersome architecture.

5. Increased automation:

Automation is gaining momentum in the insurance industry, but it will become more prominent across the entire customer experience and the value chain. The use of chatbots for processing simple requests, quoting and analysing complex issues that might need human intervention will all be a part of advanced automation.

6. Hyper-personalised policies:

The one thing that Insurtech will surely see in 2019 are policies that are customised depending on the needs of the insurants. The agents will make use of the predictive and data analytics to create policies that are cost-effective and useful for both the parties irrespective of their policy size.

All the predictions stated above will play a vital role in Insurtech in 2019, and one can expect accelerating growth in fields like AI, automation of processes and digitalisation of the underwritings. Data and analytics is the underlying key that will enable all these advancements to take place smoothly.

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

By : Sampada S M

Published on Apr 1, 2025

AI AI Hallucinations Artificial Intelligence Reliable AI 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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