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Contactless Solutions in Insurance

3 minutes, 53 seconds read

Last decade was benchmark for contactless technology, which was mainly confined to payments. In 2014, with the launch of ApplePay followed by Android Pay and Samsung Pay, digital wallets played an important role in raising the bar for digital payment experiences. Another remarkable breakthrough in the contactless payments can be attributed to NFC-only debit cards introduced in 2016 by Erste Group Bank AG.

Now (the 2020s), we’re about to witness another disruption in contactless digital experiences, which will cover many different business spheres including insurance. 

However, prolonged lockdowns and the need for social distancing amidst the COVID crisis has shifted consumer preference towards digital. Consumers are now ready to adopt digital technologies — appreciating the contactless approach by Insurers.

Today’s consumers expect personalization, convenience, and greater levels of customer service satisfaction regardless of insurers, assets, and geography. Soon, we may resume socializing, but there sure will be a change in the way we interact with our environment. 

This article highlights the emerging contactless solutions in Insurance.

Claims Inspection

Going by the traditional physical inspection way, even a simple motor claim may take 5-7 working days. For instance, after a customer has intimated the insurer about the accident, the Insurer would assign a surveyor to assess the extent of damage/loss and authenticate the incident. 

This process is not only time consuming, but also requires the surveyor to visit the location, assess the damage, and process documents. 

Self-service claims portals can help customers register, inspect, and settle their motor insurance claims in a comparatively shorter time. It also eliminates field-visits for the surveyor.

The technology that is creating an impact here is Machine Vision. It can analyze damaged parts and the severity of damage through the photographs submitted by the customers. 

Trillium Mutual Insurance, Bajaj Allianz are already using contactless claims solutions for their policyholders.

[Also read: How Machine Vision can Revolutionize Motor Insurance]

Policy Distribution

Agents have been a predominant channel for insurance distribution for decades. In 2019, the new-age tech-savvy customers posed a threat to traditional agent-based selling in Insurance. The current COVID crisis has confused businesses as to which channel to opt. The elder generation, who preferred face-to-face communication while buying a policy, planning investment, etc. are reluctant to meet people. 

In this situation, multilingual/vernacular chatbots can handle pre and post-sales queries; thus, eliminating the need for agents/RMs to meet clients and prospects physically. 

Chatbots equipped with language processing capability can be a great contactless solution for policy distribution. They can eliminate human interaction in areas such as First Notice of Loss (FNOL) and customer support.

“The new normal is when people learn how to do contactless selling. Covid-19 has brought a change in universal behavior..everybody realizes the need for social distancing, the need to go digital and this is where people are more amenable to being sold to digital. Insurers who accomplish contactless sales today are the ones who will be able to make a difference going forward.”

K V Dipu, President — Operations, Communities & Customer Experience, Bajaj Allianz General Insurance

[Also read: ‘Digital’ Insurance Broker: The case for a digital brokerage]

Another aspect of this case is equipping agents with technical knowledge and they can help clients/prospects on “how to” situations through video chats.

API Integration

In the API-based business model, apart from traditional distribution channels, 3rd party apps allow customers to buy/renew insurance policies. 

Digital wallets like PayTM and PhonePe (in India) have updated their interface to allow essential payments to the fore including insurance premiums. The API-based approach in Insurance is gaining momentum as it allows contactless payments and adds convenience for the user.

[Also read: Four New Consumer-centric Business Models in Insurance]

Contactless Solutions: Field Survey using Drones

Drones carry the ability to extract accurate field information, which can fuel real-time analytics using artificial intelligence and machine learning. MarketsandMarkets estimates the Indian drone software market to reach $12.33 billion by 2022. Drones can fulfill two strategic objectives for Insurers:

  1. Risk management: through efficient field data collection, analysis, and actionable insights 
  2. Operational costs management: through effective claims adjudication, claims processing, and customer experience.

The Future

Gradually, the world will move towards a contactless ecosystem. Most of the processes will be automated and wearables and mobile devices will dominate business-to-customer interactions. 

Automotive business, which totally relied on the dealership and offline sales has adapted itself to operate online amidst this crisis. Companies like BMW, Hyundai, Volvo, and Peugeot have already introduced contactless online sales globally.

The point is — people are giving a thought to buying an expensive asset without physically examining it. Digital channels are giving almost similar experiences as physical channels to both consumers and businesses.

In the Insurance landscape, people are open to buying policies online, and at the same time, Insurers are ready to rely on technology for claims investigation, underwriting, and fraud detection. 

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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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