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Most Innovative Insurtechs of 2023

The insurance industry has experienced significant transformations in recent years, largely driven by technological advancements and the rise of insurtech companies. These innovative startups are upending conventional insurance practices by utilizing cutting-edge technologies to boost customer experiences, streamline operations, and offer personalized insurance solutions. This blog will focus on the most cutting-edge insurtech firms of 2023 that are changing the insurance space and pushing the envelope of what is possible. Here’s a look at the most innovative insurtechs of 2023 (in no particular order):

  1. Propeller is a US-based InsurTech that provides insurance companies, consultants, and their clients with a completely automated end-to-end underwriting platform. The firm has a white-labeled URL for brokers and agents that contains around 7,000 surety bond obligations allowing both parties to get quotes, make payments, and receive their bonds in a matter of minutes.
  1. Kita is a London-based company that provides a customized portfolio of carbon insurance solutions by linking insurance and carbon markets. The company offers a portfolio of insurance products that lower carbon risk, allowing high-quality carbon projects to scale up. Reduced risk in carbon credit transactions leads to greater flows of upfront capital and accelerates the pace of positive climate impact. Their Carbon Purchase Protection Cover insurance policy secures buyers of forward-purchased carbon credits against under-delivery.
  1. Goose is a Vancouver-based company that provides easy, affordable, insurance solutions via mobile-first self-serve platforms. Customers may purchase Life Insurance, Cancer Insurance, Critical Illness Insurance, Travel Insurance, and more using the Goose Insurance Super-App in just a few seconds without the need for a medical exam or an agent.
  1. Thimble is a US-based insurtech platform that enables small businesses like handymen, landscapers, DJs, artisans, and event planners to purchase insurance coverage by job, month, or year using an app, website, or phone. The users can also modify, pause, or cancel it right away regardless of whether the business is strong and also pick how they wish to pay before upgrading once the business truly takes off. 
  1. Wefox Holding AG, a Berlin-based firm provides customers with an insurance check tool that identifies the risks they face. 

The users receive an accurate percentage across 4 separate categories that reflect their individual level of risk.

  1. NEXT Insurance is a California-based firm that provides small businesses like pet care providers, Amazon sellers, engineers, architects, etc. with specialized and affordable insurance solutions. The firm is also working on creating a digitally embedded payroll experience for small businesses across the U.S. which will help them effectively manage cash flow and only pay for the coverage they require.
  2. Dacadoo is a Swiss tech firm that combines mobile technologies, social networking, gamification, etc., to help users with their health and well-being through personalization. Their mobile-first digital health engagement platform encourages users to lead more active lives by combining social networks, online gaming, and behavioral science-based motivating strategies with artificial intelligence and automated coaching. The platform uses the Health Score, a scientifically derived number ranging from 0 to 1,000, to quantify and assess health. It relies on the user’s physical characteristics (body), emotional state (mind), and way of living (style). Rewards are given to those who lead active lifestyles. Another product is Dacadoo Risk Engine, a health risk quantification API that enables insurers and healthcare providers to examine the population’s health risk. Examples include population health management, faster underwriting, supporting pricing engines, and dynamic pricing.  

Conclusion:

The Insurtech revolution is in full swing, and these innovative companies are leading the charge. From redefining underwriting with AI and ML to pioneering usage-based insurance, enhancing customer experience, transforming claims processing with blockchain, and embracing risk management and prevention, they are reshaping the insurance industry as we know it. With a growing focus on technology, data, and customer-centric approaches, the future of insurance sure looks promising.

(Note: The insurtechs highlighted here are not rank-based and are not indicative of the ‘best’ insurtech products available today.)

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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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