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InsurTalks Podcast with Dr. Robin Kiera: Nurturing Sales in the Global Shutdown

9 minutes, 45 seconds read

Today, we are facing a crisis unlike ever before. The outbreak of COVID-19 has shaken the very foundation of the world economy. Social distancing and lockdowns have disrupted supply chains and international trade. 

We are seeing an unprecedented surge in lay-offs since the last economic recession in 2008. Amongst other industries that have been affected, Insurance and InsurTech are also finding it difficult to deal with the crisis. To get deeper insights on the new strategy for Insurance Sales, we interviewed Dr. Robin Kiera, founder of  Digitalscouting and renowned Insurtech & Fintech Influencer.

Digitalscouting is a platform with over 70,000 followers for thought leaders, entrepreneurs, and senior managers in finance and insurance to share best practices, lessons learned, and up-to-date views on tech and business trends around the world. 

Dr. Robin is a Co-author of The InsurTECH Book which offers essential updates, critical thinking, and actionable insight globally from startups, incumbents, investors, tech companies, advisors, and other partners in this evolving ecosystem, in one volume. He also advises and mentors many start-ups and corporations in various industries. His other areas of expertise include Brand & Marketing, Public Relations, Business, and Management. 

Connect with Dr. Robin Keira- LinkedIn

Complete podcast:

Excerpt from the interview-

Impact of COVID-19 on Insurance in Europe

Europe’s Insurance markets are facing a severe crisis? How are carriers, agencies, and insurance professionals in Europe developing measures to survive the global shutdown?

Dr. Robin: We have several live shows that we do in English and German where we interview several CXOs in the insurance industry. Mr. Christopher Lohmann from Gothaer Insurance said that only 10% of sales are doing well. 

So the question is, what do they do well? 

The answer to that is, they talk to clients, change their value propositions and product portfolios. 

One agent from Allianz said he is doing well because in the initial weeks of the lockdown he reached out to small and mid-sized businesses and helped them cope with the situation — displaying humility on their part. One of them helped his client in restaurant business sign-up for an online delivery service during lockdown. It wasn’t his job, however, if his client does good business, he will remain his client. Some agents and brokers took this opportunity to invest time into their client’s businesses and flourishing them. 

Other areas of distribution such as bank insurance i.e. selling insurance via banks are facing losses as most retail banks are closed and a lot of them have not found ways to effectively communicate with their clients.  

On the claims side, we haven’t seen any disastrous news yet. Also, it is interesting to see from the Life Insurance segment which did not see a surge in claims for term life or whole life insurance. However, we saw general insurance lines such as car insurance going down as not many people are driving. It’s a mixed picture. While in US insurers are paying the claims, in Germany insurers have decided to wait till the end of the year to assess the state of claims. 

The holding company of AXA announced it’s revenue numbers for the 1st quarter which was (-)10%. The negative figure means that new businesses are slaughtered. On the other hand, there’s been a boost for agencies. Our clients ask us how they can help their sales team to sell insurance policies since they cannot meet during this time. There’s been a dramatic change in the way insurance sales works.

The Reinforced Insurance Sales Strategy

How can Insurers equip their agents to generate sales in this pandemic?

Dr. Robin: It depends on the philosophy of an insurance company. Many insurance companies here are very old and go as far back as the 19th century. They have a very authoritarian culture and the agent-insurer relationship is very complex. There are some trust issues due to the unethical practices. 

For example, one of the big insurers promised young people to get on board and provide them with customer portfolios. But on the day of business, they see that only half of the portfolios were provided. If there is such a toxic relationship in an organization, then it gets difficult to deliver good output. 

However, small and mid-sized insurance companies have a very nice working culture where they willingly help their sales people. They have a very effective way of communicating with each other. 

Insurance Sales in The New Normal 

What are some Attention hacking lessons for Insurers operating in ‘The New Normal’?

Dr. Robin: Let’s divide and focus on two groups. 

First is the End Customer. For example, we have a client which is a big insurer with a distribution model to banking i.e bank insurance. We looked at every single touch-point in the process and noticed that they were not present in any. So we started placing them in every touch-point. Therefore, when a user logs into his bank account, the insurer can extract their data with the demographics, individualize and present relevant content/product.

[Related: How Technology is Transforming Insurance Distribution Channels]

Second is Sales. It doesn’t make sense to spend millions of euros and dollars in advertising with celebrities. Instead, they should produce content with people they know in the insurance community such as bank managers, insurance agents, etc. However, what’s more important is how insurers communicate and manage their sales against brokers, retail employees, etc. 

One of the most unestimated channels of communication is the messenger system. For example, WhatsApp, which is very popular in the west. We started experimenting with WhatsApp two years ago and the response was amazing. 

Sales managers use this distribution channel to share content with their agents. It brings 10x or sometimes 50x better engagement than a newsletter coming from headquarters. It is very necessary to focus on the attention of the end customer but also of the sales team. There are many simple ways to engage with sales guys such as interviewing top 2-3 performers with a simple smartphone. The key is to produce massive content and share with customers and sales that they are zoomed into the company. 

[Product: Lead Generation Chatbot]

Prevailing Technological Challenges

What are some of the technological challenges faced by Insurers operating in the New Normal?

Dr. Robin: The insurance industry needs to change culturally. They need to be a part of the daily lives of its customers and agents. They need to win their hearts, minds, and home screens. Insurers need to be on the apps, on platforms, and help them provide value. For that, we need technology. 

[Related: Four New Consumer-centric Business Models in Insurance]

Technology helps in faster claims and application processes which is a given but there are still many companies in the world that do not have these standard processes automated. I believe that the true game-changer will be AI, data science, data analytics in claims or underwriting departments. 

[Report: The State of AI in Insurance 2020]

Insurance is ripe for AI. Still, why are some Insurers still hesitant to invest in AI?

Dr. Robin: Traditional Insurers overlook the capabilities of AI and other technologies. Having people both — from inside and outside of the industry can give a broader perspective on applications of technology to bring innovative products and solutions. 

Product Innovations in Insurance

What product innovations in insurance are going to take place for short-term, mid-term, and long-term?

Dr. Robin:  In the short-term, adaptation of certain payment methods like pausing the payments and lowering risks could be done. 

In the mid-term, you will see more flexible products, more lean underwriting, or more lean claim management. What we need to think is about our role in society. Our mind-set should not be of pushing insurance products down the throats of the customers. What our role is to pool risks and stand by those who have been hit by these risks. We should not just help them during claims but before as well. 

For example, why should we have to replace a car if we could send them a push notification intimating them of the hailstorm and advising to put their car inside the garage? 

Another example is, why to sponsor super expensive healthcare when we can help the people to live healthier lives. Having customers call you portrays that the insurer is the solution. And that is what I believe the long-term goal should be. 

COVID-19: An Impetus to Digital Processes?

Insurers are taking the distribution process online. How are the Insurers adjusting to this new model? Is the ongoing pandemic, going to be the impetus for insurance to move completely digital? 

Dr. Robin:  Having come so far to 2020, I think it is very rude to do time-consuming tasks. What is needed here is to educate the people that their manual tasks can be done by machines. Today, there is so much knowledge available out there. One of my clients asked me how much we should invest in educating employees. To which I said, zero. You don’t have to hire so many consultants to educate them, just send them relevant YouTube videos. 

Way Forward for InsurTech

InsurTech largely facilitates technology in terms of scale, distribution, and market fit. How will New Normal InsurTech create market attractiveness?

Dr. Robin: The first thing that InsurTech or any other company needs to understand that all the plans made earlier have now become irrelevant. It’s time to research and build value propositions for existing clients and prospects. This would include a new pricing and pilot structure as well. 

For example, we can create a small package of services which an insurer can easily download and try out. I don’t think they should aim for $100-$200 million deals but rather give this small package demo to insurers trying to get them hooked on it. This will pave the way for getting more business. I have seen some software companies slashing prices to 70%-80% in some cases. It’s not always the question of pricing, but when InsurTechs say that they’re not able to sell, it ‘s time to re-evaluate their pricing models.   

Road to Recovery for General Insurance

Many General Insurance lines are hit- Travel, Motor, Home – what will be the road to recovery for these Insurance lines?

Dr. Robin: It’s going to be difficult. I was talking to Chris Skinner, an author in the finance space where he said that it’ll take two years to recover. This is a significant paradigm shift in Insurance Sales. Maybe we will never go back to normal. I used to travel a lot for business. Right now, I am very happy to do remote workshops. It is unpredictable how it’s going to pan out.  

The Expert Advice

First, it is time to bypass the moment of shock. Most insurers have done that. They are past the crisis mode and are addressing the issues at hand. 

Second, avoid watching too much news about the coverage on COVID-19. It’ll just bring the morale down.

Third, Stay Zero! and re-evaluate everything- what kind of products we should make, what do clients need, which clients have money to invest, what additional products can we launch, what should be the pricing model, what kind of value proposition we should provide and

Fourth, is to take action rather than Netflix. There will be massive layoffs, so it’s important to ensure you bring value to the company. 

Last but most important, is to focus on the attention of the end customer and provide value to them. Make your customer come to you.


AI is going to be essential for Insurers to gain that competitive edge in the post-pandemic world. Check out Hitee — an Insurance specific chatbot for driving customer engagement. For your specific requirements, please feel free to write to us at hello@mantralabsglobal.com.

Podcasts in this series:

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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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