What are the latest Innovations in InsurTech

Published on Nov 21, 2018

Updated on Oct 16, 2019

The technological transformation has slowly and steadily paved its way into the Insurance sector and has started to disrupt it gradually. I have mentioned continuously some of the most astonishing technological innovations that involve AI and machine learning and other latest innovations. But, when we talk about InsurTech, then it is one of those sectors which got a bit delayed to see the light of technical advancement. Nevertheless, Insurtech is still witnessing an enormous number of innovations some of which are already in use while some of them are underway. Innovation in insurance is happening, and the next decade will see a transformation in how the entire industry operates.

Here are some of the most significant insurance innovations in InsurTech:

1. Drones

Drones are a quite popular unmanned aerial vehicle in the aviation industry. It is equipped with many technological benefits that caught the eye of Insurance companies about five years ago. InsurTech companies started experimenting with Drones and found its application in claim adjustment, and large-scale surveying because of its small size and effortless manoeuvring.

Reasons why it is helpful for insurance companies:

• Roof damage inspections: Drones are useful for rooftop damage inspection which is touted to be one of the most dangerous and difficult inspections. In cases of fire accident or crazily high rooftops, the difficulty level is even more. Rather than sending an army of men to inspect the notoriously dangerous roofs, an adjuster can use a drone equipped camera and take the pictures of the entire rooftop without actually visiting the location physically.

• Large spaces: Drones can also be used for inspecting extensively large areas like warehouses and farmlands.

• Integration with other technologies: The images that are taken by drones can be integrated with AI-based applications and other technologies to assess the damage and repair costs.

2. Smart Homes

Insurance companies have understood the importance of technological tools that not only safeguard the customers but also reduce the total number of claims. This thought has given rise to several partnerships between insurance companies and smart home technology companies. For example, Insurance firm Allstate and farmers have developed applications for Amazon echo that helps to analyse the insurance coverage.

A well-connected home is a win-win for both the consumer as well as the insurers. Digital sensors around the house provide the resident with real-time alerts. So, damage can be minimised and sometimes eliminated resulting in insurers paying lower costs and customers having lesser premiums. Also, the smart homes allow greater data collection points that can be used to create the consumer profile based on his habits leading to an accurate underwriting and affordable coverage.

3. Quantum Computing

While AI has a significant influence on the Insurance industry it is still restricted by barriers posed by binary computing. Quantum computing is the answer to those challenges, and it is changing the entire dynamics on how insurance companies carry out complex calculations. Insurtech companies are creating, and testing solutions around this approach and its effects will soon be visible.

4. Smart Contracts

A smart contract is an electronic document that is capable of executing itself based on a set of agreed pre-defined conditions and clauses. Non-adherence to any of the requirements results in penalties as in a traditional legal document. It is an intelligent way to create and process policies online with strangers without the involvement of a third party. Japanese insurance company Tokio Marine & Fire Insurance Nichido together with NTT data has already started to test blockchain technology for defining policies for sea-based business exchanges.

5. Telematics Insurance

Telematic insurance car products are similar to black boxes. A telematics device equipped with GPS, SIM, motion sensors and an analytic software is installed in a car to determine the driving patterns of the driver. The telematics box collects and processes all this data and send this to the insurance companies. With the help of this data, the insurance companies create tailored insurance plans for their insurants. This service prevents companies from using the “one size fit all” approach and help to create a more sophisticated and specific insurance plan.

The insurance sector is on its way of digital transformation and customers are also expecting the same from their insurance providers. IoT, wearables are some of the other innovations that are in the nascent stages of development, but soon we can expect them to become a major part of InsurTech innovations.

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