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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. 

Podcasts in this series:

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Silent Drains: How Poor Data Observability Costs Enterprises Millions

Let’s rewind the clock for a moment. Thousands of years ago, humans had a simple way of keeping tabs on things—literally. They carved marks into clay tablets to track grain harvests or seal trade agreements. These ancient scribes kickstarted what would later become one of humanity’s greatest pursuits: organizing and understanding data. The journey of data began to take shape.

Now, here’s the kicker—we’ve gone from storing the data on clay to storing the data on the cloud, but one age-old problem still nags at us: How healthy is that data? Can we trust it?

Think about it. Records from centuries ago survived and still make sense today because someone cared enough to store them and keep them in good shape. That’s essentially what data observability does for our modern world. It’s like having a health monitor for your data systems, ensuring they’re reliable, accurate, and ready for action. And here are the times when data observability actually had more than a few wins in the real world and this is how it works

How Data Observability Works

Data observability involves monitoring, analyzing, and ensuring the health of your data systems in real-time. Here’s how it functions:

  1. Data Monitoring: Continuously tracks metrics like data volume, freshness, and schema consistency to spot anomalies early.
  2. Automated data Alerts: Notify teams of irregularities, such as unexpected data spikes or pipeline failures, before they escalate.
  3. Root Cause Analysis: Pinpoints the source of issues using lineage tracking, making problem-solving faster and more efficient.
  4. Proactive Maintenance: Predicts potential failures by analyzing historical trends, helping enterprises stay ahead of disruptions.
  5. Collaboration Tools: Bridges gaps between data engineering, analytics, and operations teams with a shared understanding of system health.

Real-World Wins with Data Observability

1. Preventing Retail Chaos

A global retailer was struggling with the complexities of scaling data operations across diverse regions, Faced with a vast and complex system, manual oversight became unsustainable. Rakuten provided data observability solutions by leveraging real-time monitoring and integrating ITSM solutions with a unified data health dashboard, the retailer was able to prevent costly downtime and ensure seamless data operations. The result? Enhanced data lineage tracking and reduced operational overhead.

2. Fixing Silent Pipeline Failures

Monte Carlo’s data observability solutions have saved organizations from silent data pipeline failures. For example, a Salesforce password expiry caused updates to stop in the salesforce_accounts_created table. Monte Carlo flagged the issue, allowing the team to resolve it before it caught the executive attention. Similarly, an authorization issue with Google Ads integrations was detected and fixed, avoiding significant data loss.

3. Forbes Optimizes Performance

To ensure its website performs optimally, Forbes turned to Datadog for data observability. Previously, siloed data and limited access slowed down troubleshooting. With Datadog, Forbes unified observability across teams, reducing homepage load times by 37% and maintaining operational efficiency during high-traffic events like Black Friday.

4. Lenovo Maintains Uptime

Lenovo leveraged observability, provided by Splunk, to monitor its infrastructure during critical periods. Despite a 300% increase in web traffic on Black Friday, Lenovo maintained 100% uptime and reduced mean time to resolution (MTTR) by 83%, ensuring a flawless user experience.

Why Every Enterprise Needs Data Observability Today

1. Prevent Costly Downtime

Data downtime can cost enterprises up to $9,000 per minute. Imagine a retail giant facing data pipeline failures during peak sales—inventory mismatches lead to missed opportunities and unhappy customers. Data observability proactively detects anomalies, like sudden drops in data volume, preventing disruptions before they escalate.

2. Boost Confidence in Data

Poor data quality costs the U.S. economy $3.1 trillion annually. For enterprises, accurate, observable data ensures reliable decision-making and better AI outcomes. For instance, an insurance company can avoid processing errors by identifying schema changes or inconsistencies in real-time.

3. Enhance Collaboration

When data pipelines fail, teams often waste hours diagnosing issues. Data observability simplifies this by providing clear insights into pipeline health, enabling seamless collaboration across data engineering, data analytics, and data operations teams. This reduces finger-pointing and accelerates problem-solving.

4. Stay Agile Amid Complexity

As enterprises scale, data sources multiply, making Data pipeline monitoring and data pipeline management more complex. Data observability acts as a compass, pinpointing where and why issues occur, allowing organizations to adapt quickly without compromising operational efficiency.

The Bigger Picture:

Are you relying on broken roads in your data metropolis, or are you ready to embrace a system that keeps your operations smooth and your outcomes predictable?

Just as humanity evolved from carving records on clay tablets to storing data in the cloud, the way we manage and interpret data must evolve too. Data observability is not just a tool for keeping your data clean; it’s a strategic necessity to future-proof your business in a world where insights are the cornerstone of success. 

At Mantra Labs, we understand this deeply. With our partnership with Rakuten, we empower enterprises with advanced data observability solutions tailored to their unique challenges. Let us help you turn your data into an invaluable asset that ensures smooth operations and drives impactful outcomes.

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