Can Distributed Ledgers Accelerate Insurance Workflows?

Published on Aug 6, 2019

Updated on Dec 13, 2019

The years 2018-19 are the banner years for the US$ 5.17 trillion global insurance sector. However double-booking, counterfeiting, and premium diversions through unlicensed brokers still throb insurance companies. And one of the prime reasons for such unethical activities is the lack of tight coupling between stakeholders. A simple solution to these challenges is distributed ledgers- a contemporary technology that ensures transparency. Distributed ledger technology in insurance can create a collaborative environment for handling information, minimizing instances of fraudulent activities.

How Can Distributed Ledgers Accelerate Insurance Workflows?

Where most insurtech startups and small insurers are looking for “insurance-in-a-box” technology, big players demand bespoke technology to develop distinct capabilities for customer convenience and manage their enterprise workflows. Fortunately, distributed ledger technology solves a major chunk of this problem.

For startups and small to medium size insurtech firms, cloud-based, customizable workflow management products can simplify the processes and create a collaborative work environment. Large enterprises can, of course, afford time and investment for tailor-made technologies suitable for their overall business requirements.

#Smart Contracts

Smart contracts can automatically determine whether to transfer an asset to the nominee or back to the source, or a combination of both. It does not necessarily create a contract or legal act, but can sure validate a condition. For example, Ethereum provides a prominent smart contract framework.

Smart contracts allow credible transactions with or without involving third parties (oracles).

For example, Etherisc uses smart contracts concepts for building insurance products. The fundamentals used for Etherisc’s insuring flight delays product is applicable for insurance products like crop insurance, flood, earthquake, etc.

#Claims Management

Cifas reports a 27% rise in false insurance claims across the UK in the past year. Moreover, insurers identify 1 in every 30 claims as fraudulent. Organizations can track records better with distributed ledgers minimizing the illicit instances.

Blockchain technology allows for automated real-time data collection and analysis. BCG expects Property and Casualty (P&C) insurance has the potential of processing claims up to 3x faster and 5x cheaper than traditional processes.

It can also enhance customer experience by removing indirections due to various touchpoints between him and the claim settlement manager. Distributed ledgers can overall benefit processing time, automating payments, eliminating trust issues, and fraud reduction.

Traditional Insurance Model vs Distributed Ledger Insurance Model: Distributed Ledger Technology in Insurance

#Reinsurance

Reinsurance (passing a whole or part of insurance liabilities to another company) will simplify the sharing of data like bordereau and claims databases. For the insurance companies not preferring to share their client’s data, access rights can be customized in distributed ledgers.

According to PWC research, the reinsurance industry can save up to $10B by increasing operational efficiencies through distributed ledgers.

#Underwriting

“A shared, distributed ledger lends itself to this need for exchanging transparent, trustworthy data in a standard format in real-time.”
Stefan Schrijnen: Director, Insurance, EY

Having accurate real-world data can help underwriters reduce paperwork and measure the assets and risks effectively.

Insurwave, a blockchain-enabled insurance platform uses a distributed database with secure access for insuring shipments across the world. Maersk, the world’s leading shipping and logistics company have partnered with Insurwave for insurance renewal of its fleet of 800 container ships.

In the words of Lars Henneberg, Head of Risk Management at A.P. Moller – Maersk. “A simple dashboard gives us a live overview of how our assets are insured, and our brokers and insurers have access to the same overview. If the location, cargo, or other data about our ships changes, everyone is notified — no delays, no paperwork, no mistakes.”

#Product Design using Distributed Ledger Technology in Insurance

Instead of all-encompassing insurance policies, consumers look for short, custom-built policies that satisfy their immediate needs. Therefore, to stay competitive, insurance companies (and even e-commerce startups) need to consistently build new and relevant insurance products. Expanding features or building new products on the same fundamentals can be effectively realized with strong and transparent ledgers.

AXA’s smart contract product Fizzy is a next-generation Parametric Insurer, which uses transparency as its USP. It provides travel insurance on flight delays and cancellations. The claims displayed on the website are stored in a blockchain and no one can change the terms after purchase. User can buy the insurance online. When the flight is delayed or canceled, the public databases of plane status information automatically triggers the insurance holder’s compensation. The event confirmation executes and closes the claim process instantly.

Precautions to Take With Distributed Ledgers in Insurance

Enterprises should be cautious about sharing access rights on distributed ledgers.
Blockchain transactions are irreversible, therefore every click from an authorized user should be mindful.
Instead of mimicking a trend, insurance companies can deploy the distributed ledger technology to best suit their business requirements.

Conclusion

MarketsandMarkets expects blockchain technology’s share in the insurance market to reach $1.4 billion by 2023.

The insurance industry has already deployed distributed ledger components for insuring flight delays, lost baggage claims, and is expanding to shipping, health, and consumer durables domains.

The future can also witness blockchain, AI, drones, and robotics disrupting the insurance industry together.

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?

Can Distributed Ledgers Accelerate Insurance Workflows?