Astronaut loading animation Circular loading bar

Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(20)

Clean Tech(5)

Customer Journey(12)

Design(36)

Solar Industry(6)

User Experience(56)

Edtech(10)

Events(34)

HR Tech(2)

Interviews(10)

Life@mantra(11)

Logistics(5)

Strategy(17)

Testing(9)

Android(47)

Backend(30)

Dev Ops(7)

Enterprise Solution(27)

Technology Modernization(2)

Frontend(28)

iOS(43)

Javascript(15)

AI in Insurance(35)

Insurtech(63)

Product Innovation(49)

Solutions(19)

E-health(10)

HealthTech(22)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(132)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(7)

Computer Vision(8)

Data Science(17)

FinTech(50)

Banking(7)

Intelligent Automation(26)

Machine Learning(47)

Natural Language Processing(14)

expand Menu Filters

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 – 

Cancel

Knowledge thats worth delivered in your inbox

10 Analytics Tools to Guide Data-Driven Design

Analytics are essential for informing website redesigns since they offer insightful data on user behavior, website performance, and areas that may be improved. Here is a list of frequently used analytics tools to guide data-driven design that can be applied at different stages of the website redesign process. 

Analytics Tools to Guide Data-Driven Design

1. Google Analytics:

Use case scenario: Website Audit, Research, Analysis, and Technical Assessment
Usage: Find popular sites, entry/exit points, and metrics related to user engagement by analyzing traffic sources, user demographics, and behavior flow. Recognize regions of friction or pain points by understanding user journeys. Evaluate the performance of your website, taking note of conversion rates, bounce rates, and page load times.

2. Hotjar:

Use case scenario: Research, Analysis, Heat Maps, User Experience Evaluation
Usage: Use session recordings, user surveys, and heatmaps to learn more about how people interact with the website. Determine the high and low engagement regions and any usability problems, including unclear navigation or form abandonment. Utilizing behavior analysis and feedback, ascertain the intentions and preferences of users.

3. Crazy Egg:
Use case scenario: Website Audit, Research, Analysis
Usage: Like Hotjar, with Crazy Egg, you can create heatmaps, scrollmaps, and clickmaps to show how users interact with the various website elements. Determine trends, patterns, and areas of interest in user behaviour. To evaluate various design aspects and gauge their effect on user engagement and conversions, utilize A/B testing functionalities.

4. SEMrush:

Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct keyword research to identify relevant search terms and phrases related to the website’s content and industry. Analyze competitor websites to understand their SEO strategies and identify opportunities for improvement. Monitor website rankings, backlinks, and organic traffic to track the effectiveness of SEO efforts.

5. Similarweb:
Use case
scenario: Research, Website Traffic, and Demography, Competitor Analysis
Usage: By offering insights into the traffic sources, audience demographics, and engagement metrics of competitors, Similarweb facilitates website redesigns. It influences marketing tactics, SEO optimization, content development, and decision-making processes by pointing out areas for growth and providing guidance. During the research and analysis stage, use Similarweb data to benchmark against competitors and guide design decisions.

6. Moz:
Use case scenario: Research, Analysis, SEO Optimization
Usage: Conduct website audits in order to find technical SEO problems like missing meta tags, duplicate content, and broken links. Keep an eye on a website’s indexability and crawlability to make sure search engines can access and comprehend its material. To find and reject backlinks that are spammy or of poor quality, use link analysis tools.

7. Ahrefs:
Use case scenario:
Research, Analysis, SEO Optimization

Usage: Examine the backlink profiles of your rivals to find any gaps in your own backlink portfolio and possible prospects for link-building. Examine the performance of your content to find the most popular pages and subjects that appeal to your target market. Track social media activity and brand mentions to gain insight into your online reputation and presence.

8. Google Search Console:

Use case scenario: Technical Assessment, SEO Optimization
Usage: Monitor website indexing status, crawl errors, and security issues reported by Google. Submit XML sitemaps and individual URLs for indexing. Identify and fix mobile usability issues, structured data errors, and manual actions that may affect search engine visibility.

9. Adobe Analytics:
Use case scenario:
Website Audit, Research, Analysis,
Usage: Track user interactions across multiple channels and touchpoints, including websites, mobile apps, and offline interactions. Segment users based on demographics, behavior, and lifecycle stage to personalize marketing efforts and improve user experience. Utilize advanced analytics features such as path analysis, cohort analysis, and predictive analytics to uncover actionable insights.

10. Google Trends:

Use case scenario: Content Strategy, Keyword Research, User Intent Analysis
Usage: For competitor analysis, user intent analysis, and keyword research, Google Trends is used in website redesigns. It helps in content strategy, seasonal planning, SEO optimization, and strategic decision-making. It directs the production of user-centric content, increasing traffic and engagement, by spotting trends and insights.

About the Author:

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot