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InsurTalks Podcast with Andrew Warburton: Delivering value-added experiences in the New Normal

5 minutes, 26 seconds read

The outbreak of pandemic Covid-19 has disturbed the political, social, economic, and financial structures of the whole world. The analysis by the UN Department of Economic and Social Affairs (DESA) said the COVID-19 pandemic is disrupting global supply chains and international trade.

To understand the impact of this crisis on the Insurance and InsurTech industries, we interviewed Mr. Andrew Warburton, Sales Director, Winsure Financial to get a sense of the current situation and understand “the new normal in Insurance”. 

Mr. Andrew Warburton is a Sales Director for Winsure Financial in London, a company that specializes in providing innovative investment vehicles that can be distributed digitally to clients or through professional advisors. He is also an advisor for Insurtech Hub in Istanbul. With over 30 years of experience in the global Insurance/Banking industry he believes that Insurtech and fintech are the only way forward to be relevant in the new digital age. Andrew has an international Sales and Marketing background working in Senior Executive positions with large multinationals in 6 countries.

Connect with Mr. Warburton – LinkedIn

The excerpt from the interview with Mr. Andrew Warburton:

The Impact of COVID-19 in the Insurance Industry

Almost every business has been affected by COVID-19 severely. What are the direct and indirect implications on Insurance?

Indeed the COVID-19 pandemic has deeply impacted the market. In Turkey as well, there’s a drastic reduction in new businesses. There are 3 major areas of impact due to this crisis in Insurance-

Claims– There’s been a spike in claims especially in Travel, Health, and Life Insurance lines. Death rates in western Europe and the USA might have been up by 50% on a monthly figure. The impact may not be huge as more elderly people are parting away and they don’t have the same needs as that of younger families. However, Travel Insurance has been deeply affected due to lockdowns and people avoiding travel in general as a precautionary measure. 

Customer Engagement Another area where the Insurance sector is facing a problem is how to reach customers? Selling agents are no longer welcome knocking on the door due to the lockdowns. It is very difficult for banks and insurance companies to reach their customers in the normal fashion.

Economic Slowdown– Many people are drawing negligible salaries or in some cases no salaries at all. But they still have to pay insurance premiums which are an additional burden on them. 

Insurance is a kind of business where sale is prompted in some way. It may not be the case for some Insurance lines such as car insurance which is bought online in many countries. Without that prompt, probably people won’t buy insurance. Moreover, times like these where there is a cash crunch, insurance might be the last thing in people’s minds. 

Changing Customer Preferences

In a post-pandemic World, will insurance ever be bought offline? Or have we crossed the threshold for now buying policies purely online?

It’s quite a mixed bag of what we see around the world where some countries are quite advanced in digital sales. On the other hand, some countries still prefer manual processes. In this first wave of the pandemic, developing countries have not been impacted compared to the sort of lockdown. We have seen platforms like Alibaba, Amazon, and food delivery apps where people are spending more time on it and ordering food online. Insurance too will see a similar trend towards more online sales.

Customer Expectations from Insurance

Consumers, now more than ever are seeking value-added experiences with the products & services they buy. How will these expectations amidst this Pandemic backdrop impact new product innovation within insurance? 

Many insurance companies have a lot of data about their customers such as where they live, their buying habits, etc. For example, if they have a car how many miles do they do every day, where do they go, where’s the car parked or when do they go to the airport, etc. This data has not been used in the past but it enables us to determine premium based on which part of the district they live. There’s a lot of data available, but companies are not able to extract and use it to their benefit. Companies want to invest in Artificial Intelligence and Machine Learning to understand customer behavior and give a personalized experience. That is happening currently in health insurance and car insurance. Certainly, Insurers will look forward to investing in these technologies in the coming months.

Impact of COVID-19 in AI Adoption

Many Insurance regulatory bodies are introducing sandboxes for Insurtech startups to experiment with AI and new cost optimization technologies. How does this pandemic impact the Insurance industry in terms of AI adoption? Will AI remain a priority?

Certainly AI will still be a priority. Everybody believes that AI will have the most impact on the Insurance industry. Nobody could have predicted this pandemic coming. One cannot plan for situations like these. But AI will help us cope with the pandemic better. Coming to the sandboxes, it has made it much easier for the Insurtechs to connect with Insurance companies.

Risk Mitigation Strategies in Insurance

What are the strategies to mitigate risks in insurance?

Insurers are investing in AI-driven products which require digital platforms to reach to the customers. Digital channels such as chatbots will play a key role in getting potential clients, create leads, upsell or cross-sell, etc. Many Insurers in developed countries have not invested much in digitalization. Digitalization will be a key mitigation strategy.

The New Normal in Insurance

What will be the new normal/upcoming Insurtech trends across the globe?

There are three areas in technology that are popular- Artificial Intelligence, Internet of Things, and Blockchain. The world probably is not yet ready for blockchain but AI and IoT combined have a big impact. It’s a common misunderstanding that if AI is plugged into data, it’ll create magic tricks. But it doesn’t. Digitalization is the step one and creating data is step two. What you do next to make a difference is the key which is AI. AI can be used to detect fraud and calculate premiums. IoT can help connect with clients at home and blockchain will have a huge potential in the Insurance sector.


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

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The Future-Ready Factory: The Power of Predictive Analytics in Manufacturing

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

  • Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
  • Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
  • Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

  • Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
  • Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
  • Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

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