Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(32)

Technology Modernization(8)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(150)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

Disruption Vs Innovation in Insurance

3 minutes, 11 seconds read

People concerned with insurance have been using the terms- ‘innovation’ and ‘disruption’ interchangeably, perhaps because both correspond to building something ‘new’. However, there is a fine line between the two. All disruptors are innovators whereas, not all innovators are disruptors. Let’s delve deep into the difference between disruption and innovation in insurance.

Who are the ‘Disruptors’ in Insurance?

Disruptors drastically alter prevalent businesses, services, or products. They tend to be more creative, useful, impactful, inexpensive, time-savvy, and most importantly – scalable. 

As an example, Lemonade took in $57 million in premium revenue from 4,25,000 customers in 2018. This four-year-old startup was able to sell premiums to millennials- 90% of whom were purchasing insurance for the first time.

Reason- instead of an all-encompassing insurance package, Lemonade is keen on distributing micro policies as low as $5, which the customer perceives as useful. They’ve simplified the claim settlement process and within 3 minutes, a customer can get his refund credited to his account. While Lemonade sells its insurance policies through chatbot Maya, chatbot Jim handles claim settlement. Such AI-powered bots can handle multiple customer requests just as human agents and are better in detecting fraud. 

The disruptors are prone to adapt to changing customer preferences, which the traditional insurers are reluctant to because of the fear of losing existing customers. Disruption in insurance can break the barrier of the lower market penetration rate.

What’s Innovation in Insurance? 

Innovation is independent of drastic changes in businesses. It focuses more on bringing positive business development by delivering convenience to the customer and improving operational efficiency.

Innovation is not always about introducing new technology. It is also about harnessing existing technologies to build innovative solutions. For instance, blockchain technology has been there for decades; but the insurance industry has recently utilized it for algorithmic trading, smart contracts, policy distribution, and claim settlements.

For example, AXA Fizzy provides paperless flight insurance based on blockchain technology. Every user interaction is recorded and executed in the ledgers- from buying a policy to claim settlement without any human intervention. 

Other examples of innovation in insurance include Robo-advice, NLP (Natural Language Processing) to understand customer queries, insurance for IoT devices, AI-powered underwriting, automating insurance workflows, and Machine Learning technologies.

Also, read – Innovative insurance products of 2109

However, according to McKinsey’s report on Digital insurance in 2018, most of the P&C, health, and life insurance innovations revolve around marketing and less towards product development and claims. This gives an idea of the scope of innovation in insurance.

Where insurtechs are focusing

What’s Next in Insurance: Disruption or Innovation?

The traditional insurance business is known to be resilient to technological advancements and innovations in terms of consumer-centric products. To stay relevant and competitive, insurers should shift focus from digitization to strategic disruption according to the changing market dynamics.

In fact, Insurers are willing to fund insurance startups to gain a first-mover advantage in terms of technology and innovations. These investments illustrate a clear goal of improving customer experiences and supporting their existing operations at the startups’ risk. 

For instance, “Axa provided seed funding for five European start-ups under a fund set up in France in 2013, before launching Axa Strategic Ventures in 2015. The €200 million ($223.47 million) venture capital fund has the mandate to invest in innovations in insurance..”. (OECD (2017), Technology and innovation in the insurance sector)

Innovation from Insurtechs has the potential to contribute to the insurance value chain; however, managing disruption is still quite a challenge. Disruption alters the business and behaviours in such a short span that most of the outcomes remain unanticipated. While innovation takes time to catch the stream, disruption can make or kill a business. The best is to blend incumbents’ years of experience with innovation from startups to bring an accountable disruption.

We’ve been solving critical front & back-office insurtech challenges through innovative technological solutions. Drop us a ‘hi’ at hello@mantralabsglobal.com to know more.

Cancel

Knowledge thats worth delivered in your inbox

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?

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot