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4 Key Takeaways from India Insurance Summit & Awards 2020

The India Insurance Summit & Awards 2020, themed around technology and innovations in Insurance concluded on March 13th in Mumbai. The event witnessed enthusiastic participation from corporates like Future Generali India Life Insurance, ICICI Lombard, Aditya Birla Sun Life Insurance, Pramerica Life and many more. The stalwarts from the Insurance industry addressed the tech-powered revolution that is soon to happen with Digital 2.0. Here are 4 key takeaways from IISA that highlight the future of Insurance and InsurTech.

1. Digital 2.0 is on rise

Accenture’s research report on the post-digital era reveals that 94% of businesses have accelerated their digital transformation over the past three years. While the era of Digital 1.0 was focused on the mobile, simplified design and a wider range of applications, Digital 2.0 extends the ecosystem into the next-gen interface which relies on anywhere, anytime and any platform mindset.

The traditional insurance distribution channels have already received a digital facelift; with Digital 2.0, they tend to become more consumer-focused and experience-driven. Insurers are empowering distributors to deliver next-gen experiences to customers and deliver products & services for Micro-Moments

[Related: How technology is transforming Insurance distribution channels]

2. Millennials are characterized by Micro-Moments

Micro-Moment is an intent-rich moment when a person turns to a device to act on a need — to know, go, do, or buy” (Google).

An average consumer experiences hundreds of micro-moments throughout the day. More than 91% of smartphone users use mobile phones for inspiration in the middle of a task. People are becoming more research-obsessed and almost every decision made online is informed. For instance, 51% of digital consumers have purchased from a company other than their intended brand, solely based on the information they find online. Moreover, 62% of people are more likely to take an action (like purchase decision) right away even in the middle of some other task.

Earlier, customers used to view the lowest priced product as their best value for money option. Now, the customer’s ability to research is leading to higher-priced products being bought because of the greater perceived value of the product.

As a notion, Insurance is not bought; it’s sold. Thus, micro-moments present immense opportunities to engage with the customer during their buying journey. By leveraging the right points of interaction, Insurers can propose relevant and personalized insights to win customers.

[Related: Millennials and Insurance beyond convenience]

3. Online is best for small-ticket insurance 

Small-ticket insurance (or bite-size cover) focuses on the specific needs of consumers. These are characterized by low premium, low cover and hence lower profit margins. Thus, offline distribution, which involves agents and brokers isn’t feasible. Online channels with emerging API-based distribution and marketplaces are best for distributing small-ticket insurance products. In India, companies like Toffee Insurance, MobiKwik and Digit Insurance provide bite-size insurance. 

Within life insurance, term plans are sold the most online. Insurers have observed that online customers buy more and stay longer with the brand as compared to offline customers. In general, online products are more compelling. The key is — small market, great margins and greater profitability.

Moreover, small-ticket insurance delivers two-fold benefits. Consumers, who haven’t bought an insurance product before, need not pay lengthy premiums (also beneficial to Insurers for customer acquisition); while Insurers find it easier to predict customer behaviour online, allowing them to underwrite risks more accurately.

4. Technology will enhance post-sale moments of truth

Insurers have already started to utilize technologies like NLP to build self-service policy renewal/inquiry portals, AI for zero-touch integrated claims, to name some. The behaviour of the same customer on different channels (like Twitter, Instagram, LinkedIn etc.) is unique. Carriers have to map and understand these behaviours to create better-individualized journeys. Distributor journeys also play a crucial role in analysing post-sale moments of truth. Insights from distributor journey can help Insurers modify/add products into the chain based on buyers’ experiences.

Technology is also helping Insurers participate in a connected information ecosystem. Data from geo-tagging of accidents can be shared with law enforcement to understand areas prone to accidents, underlying causes and even catching criminals through facial recognition technology. For instance, Staqu Technologies, a Gurugram-based AI startup, is providing facial recognition systems to many state government police departments.

Wrapping up

Although 94% of urban and 24% of the Indian rural populace use the internet, Insurers still rely heavily on offline third-party insurance sold by agents (e.g. third party motor insurance for the rural market).

Even though online is cheaper than offline, customers prefer offline as it has more accountability. What drives offline to online is understanding that every customer is unique with unique needs and unique propositions. The truth of the matter is — when things fail, online becomes harder for customer acquisition. AI and Automation has allowed for significant cost reduction and process efficiency gains across the value chain for carriers. However, AI should be used strategically to augment processes that cannot be entirely automated so as to not fully eliminate the human in the loop, in order to better assist customers (eg: speaking to an actual person for resolving complex issues.)

Mantra Labs was a proud customer experience partner at India Insurance Summit & Awards 2020. During the event, Mantra unveiled the Internet of Intelligent Experiences (IOIX) illustrating the extremes to which technology can create sensory disruption in customer experiences!

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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.

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