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

Key Takeaways of Indian Insurance Summit & Awards 2019

India, despite being the 2nd most populous country on the planet, accounts for only 1.5% of the World insurance premiums, and 2% of World life insurance premiums. But, with the increasing numbers to serve, the insurance market in India promises huge growth and exciting potential – were only about 20% of Indians were insured last year.

Key challenges like market penetration, product innovation, risk and fraud need to be mitigated, for insurance players to achieve better growth, customer satisfaction and profitability.

The recently concluded Indian Insurance Summit and Awards 2019 aimed at having robust and key focused area discussions on these challenges, brought together the entire insurance industry network in front of a global audience.

Here are some of the highlights and takeaways from the two-day conference:

Key takeaways of India Insurance Summit and Awards 2019

  • Application of AI beyond claims and underwriting:

AI has paved its way far beyond claims and underwriting policies. The rising InsurTech wave is marking this change by tailoring solutions for individual customers and replacing the one-size-fits-all type of product that is currently available. AI also plays a major role in fraud detection and risk management strategies.

AI in insurance will allow carriers to deliver scalable and customized solutions for members and policyholders,”

 says Ramon Lopez, Vice President of Property & Casualty Claims and Innovation at USAA.

Although, India represents a smaller share of this market, in terms of revenue in comparison to the North American region; India, (along with the rest of Asia) is expected to outperform Europe over the next five-year period.

  • Product innovation for the ease of insurance processes:

While the insurance landscape is experiencing radical changes in product innovation; innovation in technology is the next frontier.

Predicting the probability of future losses can help insurers improve pricing and accuracy; which precisely can be useful in case of risk, with little historical data from which estimates have to be drawn. Around 44% of the insurers say that they have started deploying predictive analytics solution.

California based InsurTech, Carpe Data, has fully automated systems that leverage social media to detect claim frauds and ease out specific insurance processes. Allstate insurance partnered with Carpe Data to generate meaningful insights and help them to mitigate risks in insurance processing.

“The insurance industry is used to working with historical data—the most important                challenge before them is to move from that model to a predictive one.”

Gilles Ferreol, Managing Director, CNP Partners

Bajaj Allianz introduced usage-based auto insurance called ‘DriveSmart Service’. The service monitors the car through a vehicle tracking-device and provides relevant diagnostics data on the performance of the driver.

  • Cognitive Insurance is a new wave of innovation:

Data is a vital ingredient for going Cognitive. The cognitive insurance business is one that allows underwriters to be equipped with a repertoire of AI enabled tools, empowering them to make better and more informed decisions about their customer.AXA Insurance has implemented a Google Tensor Flow-based application to optimise pricing by predicting large-loss traffic accidents with over 78% of accuracy. By leveraging a deep analysis of their customer profiles, AXA was able to understand which clients were are at a higher risk of large-loss cases requiring payment of more than 10,000 – which means, they were able to optimize the pricing of its policies.

Cognitive computing is at the “peak of inflation” on the Gartner Hype Cycle. The Cognitive approach to insurance business after the digital insurance business is the new wave to bring innovation and transformation purpose of going cognitive was created solely with the purpose of reducing human effort and refining the existing process across various insurance verticals.

  • Use a Sandbox approach to test customer’s interest:

To keep pace with the fast-evolving world of InsurTech, insurance companies should consider testing their products in a controlled environment or a “Sandbox”. This approach can provide certain advantages such as allowing insurers to launch unconventional products on a pilot-basis before seeking necessary approval.

The first insurance plan launched under this method, called “Insurance Khata” was directed towards those with seasonal incomes, mostly belonging to the underserved sections of Indian society. The buyers can pool multiple single plans in one account.

 “We want insurers to think out-of-the-box,” said Nilesh Sathe, a member at the IRDAI.

This rather unique proposition encourages insurance companies to place the policyholder right at the front of their approach, consequently not allowing regulation in being a constricting force in their innovation journey.

Data, by its very nature, is both an asset and a liability, which presents inherent risks in its handling and management. Risks that can be quite severe, in a business foundationally based on dealing with uncertainties.
Insurance is one of the richest data-driven businesses, and the consequences of a data breach extend far beyond the reputational damage that results from negative news headlines.

On July 2018, SingHealth, the largest network of healthcare institutions in Singapore, came under a severe cyber-attack and the personal data of around 1.5 million patients, including those of the Singapore PM, Lee Hsien Loong, were stolen.-Straits Times reports

In the past couple of years, the insurance industry has fallen short, by being on the defensive, of handing cyber-attacks and cyber-frauds. The industry cannot afford to take be reactive for much longer – at some point, they need to be thinking ahead of their adversaries.

The non-partisan agenda of the Summit was to explore challenges and their deterrents like market penetration, product innovation, risk, and fraud. The discussions were designed to draw out clear outcomes for the industry together – in order to realize growth, customer satisfaction, profitability and deliver definitive business value. Mantra Labs was proud to sponsor the successful Summit and partake in the insightful conversations held between insurance leaders from all corners of the industry.

We hope to see you all again, in the next edition!

https://www.insurancebusinessmag.com/asia/features/interviews/protecting-the-insurance-sector-from-cyber-threats-109124.aspx

Together Towards AI: Notes from InsureTech Connect 2017

Strategic Technology Trends in Insurance

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