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5 Real-world Blockchain Use-cases in Insurance Industry

Nearly 80% of insurance executives have either already adopted or planning to pilot blockchain technology across their business units. The level of trust, transparency, and immutability that blockchain (distributed ledger technology) provides is impeccable. 

blockchain insurance use cases- benefits

Blockchain offers an independently verifiable dataset so that insurers, as well as customers, need not suffer from decisions based on inappropriate/incomplete information. In the instances of travel insurance, blockchain-based systems use external data sources to validate whether a flight was missed or canceled. Accordingly, insurers can decide on processing refund claims. Well, blockchain can handle even more complex situations of road accidents by accurately determining the vehicle or human fault.

The 5 practical blockchain use-cases in the insurance industry are-

  1. Fraud detection
  2. IoT & Blockchain together to structure data
  3. Multiple risk participation/Reinsurance
  4. On-demand insurance
  5. Microinsurance

Fraud Detection

In the US alone, every year fraudulent claims account for more than $40 billion, which is excluding health insurance. Despite digitization, the standard methods fail to recognize fraud. Blockchain can help in fraud detection and prevention to a great extent. 

Blockchain ensures that all the executed transactions are permanent and timestamped. I.e. no one, including insurers, can modify the data preventing any kind of breaches. This data can further help in defining patterns of fraudulent transactions, which insurers can use in their fraud prevention algorithms. 

Fraud detection using blockchain use case: Etherisc

Powered by smart contracts, Etherisc independently verifies claims by using multiple data sources. For example, for crop insurance claims, it compares satellite images, weather reports, and drone images with the image provided by the claimant. 

IoT & Blockchain together to structure data

As IoT will connect more and more devices, the amount of data generated from each of the devices will increase significantly. For instance, there were 26.66 billion active IoT devices in 2019 and nearly 127 IoT devices connect to the internet every second

This data is extremely valuable for insurers to develop accurate actuarial models and usage-based insurance models. Considering the auto insurance sector, the data collected about driving time, distances, acceleration, breaking patterns, and other behavioral statistics can identify high-risk drivers. 

But, the question is — how to manage the enormous data as millions of devices are communicating every second. 

And the answer is a blockchain!

It allows users (insurers) to manage large and complex networks on a peer-to-peer basis. Instead of building expensive data centers, blockchain offers a decentralized platform to store and process data. 

Multiple risk participation/Reinsurance

Reinsurance is insurance for insurers. It protects the insurers when large volumes of claims come in. 

Also read – 5 biggest insurance claims payouts in history

Because of information silos and lengthy processes, the current reinsurance system is highly inefficient. Blockchain can bring twofold advantages to reinsurers. One — unbreached records for accurate claims analysis and two — speeding-up the process through automated data/information sharing. PwC estimates that blockchain can help the reinsurance industry save up to $10 billion by improving operational efficiency.

For example, in 2017, B3i (a consortium for exploring blockchain in insurance) launched a smart contract management system for Property Cat XOL contracts. It is a type of reinsurance for catastrophe insurance.

On-demand insurance

On-demand insurance is a flexible insurance model, where policyholders can turn on and off their insurance policies in just a click. More the interactions with policy documents, the greater the hassle to manage the records. 

For instance, on-demand insurance requires underwriting, policy documents, buyers records, costing, risk, claims, and so on much more than traditional insurance policies.

But, thanks to blockchain technology, maintaining ledgers (records) has become simpler. On-demand insurance players can leverage blockchain for efficient record-keeping from the inception of the policy until its disposal. An interesting blockchain insurance use cases is that of Ryskex — a German InsurTech, founded in 2018. It provides blockchain-powered insurance platform to B2B insurers to transfer risks faster and more transparently. 

Microinsurance

Instead of an all-encompassing insurance policy, microinsurance offers security against specific perils for regular premium payments, which are far less than regular insurances. Microinsurance policies deliver profits only when distributed in huge volumes. However, because of low profit-margin and high distribution cost, despite immediate benefits, microinsurance policies don’t get the deserved traction. 

Blockchain can offer a parametric insurance platform. With this, insurers will need fewer local agents and “oracles” can replace adjusters on the ground. For example, Surity.ai uses blockchain to offer microinsurance to the Asian populace, especially those not having access to the services of banks or other financial organizations. 

For further queries around blockchain / insurance use cases, please feel free to drop us a word at hello@mantralabsglobal.com.

Related blockchain articles – 

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Smart Machines & Smarter Humans: AI in the Manufacturing Industry

We have all witnessed Industrial Revolutions reshape manufacturing, not just once, but multiple times throughout history. Yet perhaps “revolution” isn’t quite the right word. These were transitions, careful orchestrations of human adaptation, and technological advancement. From hand production to machine tools, from steam power to assembly lines, each transition proved something remarkable: as machines evolved, human capabilities expanded rather than diminished.

Take the First Industrial Revolution, where the shift from manual production to machinery didn’t replace craftsmen, it transformed them into skilled machine operators. The steam engine didn’t eliminate jobs; it created entirely new categories of work. When chemical manufacturing processes emerged, they didn’t displace workers; they birthed manufacturing job roles. With each advancement, the workforce didn’t shrink—it evolved, adapted, and ultimately thrived.

Today, we’re witnessing another manufacturing transformation on factory floors worldwide. But unlike the mechanical transformations of the past, this one is digital, driven by artificial intelligence(AI) working alongside human expertise. Just as our predecessors didn’t simply survive the mechanical revolution but mastered it, today’s workforce isn’t being replaced by AI in manufacturing,  they’re becoming AI conductors, orchestrating a symphony of smart machines, industrial IoT (IIoT), and intelligent automation that amplify human productivity in ways the steam engine’s inventors could never have imagined.

Let’s explore how this new breed of human-AI collaboration is reshaping manufacturing, making work not just smarter, but fundamentally more human. 

Tools and Techniques Enhancing Workforce Productivity

1. Augmented Reality: Bringing Instructions to Life

AI-powered augmented reality (AR) is revolutionizing assembly lines, equipment, and maintenance on factory floors. Imagine a technician troubleshooting complex machinery while wearing AR glasses that overlay real-time instructions. Microsoft HoloLens merges physical environments with AI-driven digital overlays, providing immersive step-by-step guidance. Meanwhile, PTC Vuforia’s AR solutions offer comprehensive real-time guidance and expert support by visualizing machine components and manufacturing processes. Ford’s AI-driven AR applications of HoloLens have cut design errors and improved assembly efficiency, making smart manufacturing more precise and faster.

2. Vision-Based Quality Control: Flawless Production Lines

Identifying minute defects on fast-moving production lines is nearly impossible for the human eye, but AI-driven computer vision systems are revolutionizing quality control in manufacturing. Landing AI customizes AI defect detection models to identify irregularities unique to a factory’s production environment, while Cognex’s high-speed image recognition solutions achieve up to 99.9% defect detection accuracy. With these AI-powered quality control tools, manufacturers have reduced inspection time by 70%, improving the overall product quality without halting production lines.

3. Digital Twins: Simulating the Factory in Real Time

Digital twins—virtual replicas of physical assets are transforming real-time monitoring and operational efficiency. Siemens MindSphere provides a cloud-based AI platform that connects factory equipment for real-time data analytics and actionable insights. GE Digital’s Predix enables predictive maintenance by simulating different scenarios to identify potential failures before they happen. By leveraging AI-driven digital twins, industries have reported a 20% reduction in downtime, with the global digital twin market projected to grow at a CAGR of 61.3% by 2028

4. Human-Machine Interfaces: Intuitive Control Panels

Traditional control panels are being replaced by intuitive AI-powered human-machine interfaces (HMIs) which simplify machine operations and predictive maintenance. Rockwell Automation’s FactoryTalk uses AI analytics to provide real-time performance analytics, allowing operators to anticipate machine malfunctions and optimize operations. Schneider Electric’s EcoStruxure incorporates predictive analytics to simplify maintenance schedules and improve decision-making.

5. Generative AI: Crafting Smarter Factory Layouts

Generative AI is transforming factory layout planning by turning it into a data-driven process. Autodesk Fusion 360 Generative Design evaluates thousands of layout configurations to determine the best possible arrangement based on production constraints. This allows manufacturers to visualize and select the most efficient setup, which has led to a 40% improvement in space utilization and a 25% reduction in material waste. By simulating layouts, manufacturers can boost productivity, efficiency and worker safety.

6. Wearable AI Devices: Hands-Free Assistance

Wearable AI devices are becoming essential tools for enhancing worker safety and efficiency on the factory floor. DAQRI smart helmets provide workers with real-time information and alerts, while RealWear HMT-1 offers voice-controlled access to data and maintenance instructions. These AI-integrated wearable devices are transforming the way workers interact with machinery, boosting productivity by 20% and reducing machine downtime by 25%.

7. Conversational AI: Simplifying Operations with Voice Commands

Conversational AI is simplifying factory operations with natural language processing (NLP), allowing workers to request updates, check machine status, and adjust schedules using voice commands. IBM Watson Assistant and AWS AI services make these interactions seamless by providing real-time insights. Factories have seen a reduction in response time for operational queries thanks to these tools, with IBM Watson helping streamline machine monitoring and decision-making processes.

Conclusion: The Future of Manufacturing Is Here

Every industrial revolution has sparked the same fear, machines will take over. But history tells a different story. With every technological leap, humans haven’t been replaced; they’ve adapted, evolved, and found new ways to work smarter. AI is no different. It’s not here to take over; it’s here to assist, making factories faster, safer, and more productive than ever.

From AR-powered guidance to AI-driven quality control, the factory floor is no longer just about machinery, it’s about collaboration between human expertise and intelligent systems. And at Mantra Labs, we’re diving deep into this transformation, helping businesses unlock the true potential of AI in manufacturing.

Want to see how AI-powered Augmented Reality is revolutionizing the manufacturing industry? Stay tuned for our next blog, where we’ll explore how AI in AR is reshaping assembly, troubleshooting, and worker training—one digital overlay at a time.

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