How InsurTech-Insurance Partnership Delivers New Product Innovations

4 minutes, 27 seconds read

Published on Feb 13, 2020

Updated on May 11, 2020

In 2019, InsurTech funding reached $6 billion, acknowledging the pace that technology can bring to overcome the age-old Insurance problems, the State of AI in Insurance 2020 says. While Incumbents are known for their core competencies in end-to-end insurance processes (from underwriting to claims settlement and reinsurance), InsurTechs are enticing millennials with fully digital innovative products and solutions.

The current situation can be viewed as either growing competition for traditional Insurers or an opportunity to collaborate and procure maximum benefits from each other’s competencies.

The World InsurTech Report 2019 states that nearly 90% of InsurTechs and 70% of Insurers are interested in collaboration with other InsurTechs and Insurance firms.

[Quick read: 10 Key Takeaways from the World InsurTech Report 2019]

In this article, we will discuss how InsurTech and Insurance partnership is proving beneficial for the entire ecosystem along with some successful partnership stories.

InsurTech and Insurance Partnership Benefits

A recent study pointed out that 70% of Insurance Executives are interested in collaborating with InsurTechs for developing new offerings. While developing new & innovative offerings remains the focus, such partnerships can play a crucial role in improving operational efficiency, enhancing customer experience, and increasing data capabilities.

InsurTech and Insurance Partnership outlook — *Source:* *The State of AI in Insurance*

Enabling Mobile-first Business Model

The current generation cares about self-managing everything that matters to them (including Insurance) on mobile. If it’s not convenient to use, the consumer is, perhaps, not ready to adopt it. For instance, each day, more than 5 billion people go online using their smartphones or mobile devices.

InsurTechs, as consumer-focused they are, have been leveraging mobile technologies for micropayments, mobility and IoT connectivity.

Insurer’s benefits:

Capability to extend their services/products to the mobile channel.
Attracting new customers who are more inclined towards self-service options.
Making information and services accessible and available everywhere, irrespective of geographical location, thus enhancing the customer experience.

Gaining Operational Efficiency at Scale

Insurers can harness InsurTechs’ capabilities on cutting-edge technologies like cognitive process automation, natural language processing, and ML-derived insurance analytics. Applications built using these technologies are scalable to the enterprise level.

For instance, with cognitive automation, Insurers can improve the efficiency and quality of computer-generated responses. Forrester predicts cognitive processes will overtake nearly 20% of service desk operations.

Similarly, InsurTechs are investing in developing workflow automation solutions, using which Insurers can create new automated workflows and/or customize existing workflows. Workflow automation with intelligent document and data processing capabilities has resulted in over 80% operational gains over manual processes.

Another milestone in improving operational efficiency is achieved through the adoption of chatbots. NLP-powered chatbots seamlessly integrate with an organization’s workflows and are a great way to humanize machine conversation and at the same time automate customer service portals.

Opportunity to extend the portfolio

InsurTechs still require traditional Insurers’ support for underwriting and during risk mitigation. On the other hand, Insurers are sceptical about micro and on-demand insurance because of the distribution challenges it poses for low-profit products. Insurers and InsurTechs can easily bridge the gaps and at the same time extend their range of offerings through strategic collaboration. Since 2017, Insurance and technology firms have announced more than 180 partnerships, KPMG states.

For example, American Family Insurance (AmFam) organizes its interests around innovation, advanced analytics, and connectivity. It has investments in CoverHound, HomeTap, Bunker, Wireless Registry, and LeaseLock.

“By making these investments, we do seek a financial return with the investment, but really we look for opportunities to work together, reconnaissance on how the world is changing.”
Dan Reed, MD, Managing Director, American Family Ventures

Source: Insurance Journal

Thus, InsurTech and Insurance partnership can also benefit from extending the product portfolio. Let’s now look at some remarkable examples.

4 Noteworthy InsurTech and Insurance Partnerships from Recent Years

1. Zurich Connect and Yolo

Zurich Connect, the digital arm of Zurich Italy, partnered with on-demand digital Insurance broker Yolo to provide virtual assistance to its customers. Together, they launched HomeFlix — to provide a range of Insurance coverage to renters and homeowners.

HomeFlix offers laundry service, concierge maintenance services such as plumbing and electric, and cleaning services to its customers along with regular and short-term insurance coverages starting at a nominal price of € 3.55 per month.

2. FRIDAY and Friendsurance

FRIDAY is a Berlin-based InsurTech startup. It offers digital automotive insurance with flexible terms like kilometre-accurate billing and the option to terminate at month’s end. The company partnered with Friendsurance, an online peer-to-peer insurance service provider. Friendsurance business model relies on paying out a percentage to customers who do not use (or use very little) annual insurance.

This partnership helps FRIDAY to sell at its policies on the Friendsurance platform and Friendsurance benefits from providing a range of insurance cover options to its customers.

3. Generali Global Assistance with Lyft and CareLinx

Generali Global Assistance is a division of Italy’s Generali Group. It provides travel insurance-related services. The company partnered with InsurTech Lyft and CareLinx to improve customer service and provide value-added services (e.g. CareRides, a door-to-door transportation service for special-needs individuals) respectively.

4. Prudential Singapore and StarHub

Singapore-based Prudential Insurance Company is the subsidiary of Prudential Plc, a British multinational life insurance & financial services company. The company partnered with StarHub to create FastTrackTrade — a digital trading platform. Using the FastTrackTrade platform, users can buy/sell goods, track shipments, make transactions, access financing, and buy insurance.

We’re a recognized InsurTech100 company with main focus on developing AI-first products and solutions for modern Insurance enterprises. For more details, please feel free to drop us a word at hello@mantralabsglobal.com.

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

How InsurTech-Insurance Partnership Delivers New Product Innovations

InsurTech and Insurance Partnership Benefits