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InsurTalks Podcast with KV Dipu: Protecting the Demand-side in the New Normal

7 minutes, 26 seconds read

The outbreak of Covid-19 pandemic has deeply impacted the global economy. Industries such as healthcare, travel and hospitality among others are still reeling from the immediate fallout of the crisis. The Pandemic has exposed the cracks in the Indian healthcare system, and the exposure of India’s masses to a multitude of personal risks who are largely uninsured to stave off financial ruin. At the same time, Insurance has had to adapt their processes to the fast changing climate. Core insurance functions like claims processing and customer support operations have had to accelerate transition to the cloud in order to ensure operational continuity during the crisis and adapt to the new normal. 

In this special podcast, we talk to Mr. KV Dipu about how Insurance is coping with this crisis. Before joining Insurance, he worked at GE Capital for 19 years, where he has built a career in retail finance operations. He is a certified Lean Six Sigma Black Belt and a member of the Harvard Business Review Advisory Council. Today he drives digital transformation as the President of Operations, Communities, and Customer Experience at Bajaj Allianz.

During our conversation Mr. Dipu shared valuable insights on the state of insurance, how insurers need to gear up for the challenges in the New Normal and the initiatives undertaken by Bajaj Allianz to meet their customer’s expectations.

You can watch the full podcast here: 

Interview Excerpts from Insurance in the New Normal

Potential Insurance Frauds amidst COVID-19

Insurance, at least in India, is not strange to the experience of dealing with outbreaks even though at a smaller scale – with virus outbreaks like Ebola & Zika in the past. However there aren’t too many reliable historical models to learn from and you’ve stated in the past that fraud triggers can only work if there are strong flags sitting on top of really good data. In the absence of really good data and unreliable historical models, how does this affect dealing with fraud?

KV Dipu: That’s a good question and I think this is exactly what a lot of players today across industries are grappling with because no PCP or model ever envisaged this. And if you do not have passed precedents then you have to learn as you go. So I think that is clearly what we have seen. In terms of COVID-19, you can see a series of potential fraud possibilities. 

I’m using the word ‘potential fraud possibilities’ because we have to see how they play themselves out. One is you could find a lot of people who possibly could get into scams, that they can maybe influence the entire ecosystem, especially in terms of helping customers who are seeking benefits from the insurance company or various entities. And whenever there are losses you typically will find that there are people out there who are going to try to to make a fast buck. So I think that’s one area we need to watch out for. 

The other is you will actually find that as business models emerge there are some people who’ll be quick to jump into the game. For example, today everybody feels that health insurance is one thing we should focus on and that’s typically when you could have both type A and type B errors. You have middlemen who basically promise health insurance saying ‘I can get you this.. I can have my way through various insurance companies’. You may have people trying to forge various checkups through the entire process. 

So these are some areas which we are very off, right, and the good thing is even if a model from the past is not going to help us with the specific input I think our own experience of various scenarios will come into play.I think as long as we are smart on that front it will help us. Now this is where it’s a classic combination of technology and expertise technology can enable the process but you need years of experience to figure out the fraudulent ones from the good ones. Which is where I think established companies like ours which are technically and technologically savvy, as well as years of deep expertise will be really able to figure out who the fraudsters are.

Change in the Nature of Risks & Its Impact on Underwriting

From an underwriting perspective it’s usually said that poor underwriting leads to poor financial performance, so the ‘not knowing what to expect’ will definitely have an impact on underwriting losses. Going forward, how does this change the nature of risk from perhaps the actuaries point of view? 

KV Dipu: If you look at actuarial science, what they do with every event is they learn, right. The learning adds to their kitty, so to speak. So, today you have various players globally trying to figure out what the models are, what are the potential scenarios and we can also learn from the experiences of different countries. You see while it’s still a global pandemic, the scenarios across various countries are different. Some countries for example have had a very sharp recovery, where they’ve shown a v-shaped recovery. Now there are some countries which are in a u-shape recovery pattern, and  there are some where there is a recovery-outbreak-and then a recovery which would be a W pattern. 

So I think as we see the scenarios play themselves out in various countries, we draw learnings very quickly and then basically recalibrate our models accordingly, that’s point number one. Point number two – I think once the lockdown is lifted and then when you start to see people back on the roads, when you start to see cars back on the roads, and when you start to see hospitals functioning again – that is when I think the rubber will start hitting the road and that is when our extreme vigilance will help. I think as long as we’re prepared with data it will really help us get through this.

[Related: New Product Development in Insurance: The Actuary]

Product Innovations in the New Normal

Today a lot of companies are ‘investing in digital’. They’re making sure they have digital assets, capabilities and tools not just for employees internally in the business but for outward facing agents as well. And that has been  the trend even before the Pandemic had broken out. Most sales teams and channel partners are equipped digitally with mobile apps to generate quotes, issues policies even remotely. 

Given that the physical act of selling itself has been severely affected due to lockdown restrictions and social distancing norms, How can insurance build and protect the demand side?

KV Dipu: Okay, so there is one famous whatsapp forward doing the rounds nowadays. it basically says “Guess who’s responsible for digital transformation in a company? Answer number one: CEO. Answer number two: the relevant CXO. Answer number three: COVID-19.” No prizes for guessing, right? Now what COVID-19 has done is to the point that you made everybody believe that in a push product like insurance in-person meetings, relationship building  is all important and rightly so. And that is the reason this business is intermediated and it’s been that way for a while now. New normal is where people will have to learn how to do contactless selling. That is where COVID-19 helps because if let’s say COVID-19 had been restricted to let’s say one particular city or one particular sector you would not have had a change in universal behavior. 

But the fact of the matter right now is globally right I think there are more people under lockdown than at any previous point of time in history. We have so many people on lockdown and everybody realizes the need for social distancing and the need to go digital. That is when people are also more amenable to being sold to digitally. Which is why now the smarter companies who figure out that in the new normal we have to build relationships while being physically away, and manage to sell from remote or contactless sales as i call it – are the ones who will be able to make a difference going forward. 

The good thing is from a process perspective we have enabled them like you rightly said they have the tools to generate quotes, they have the tools to issue policies, they have the tools to even raise claims. It’s about willingness and that willingness has been accelerated and fast tracked by COVID-19. So what could have potentially taken a long time has now been fast-tracked now in the last 60 days – which is why once the lockdown is lifted and we go back into the world you’ll realize that some parts of this contactless selling or even large parts of it are here to stay.

Click on the below link to watch the full episode of InsurTalks with KV Dipu –

Mantra Labs is an InsurTech100 firm building products and solutions for fast evolving enterprises. To connect with us for interviews, drop us a line 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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