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Can Distributed Ledgers Accelerate Insurance Workflows?

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

  1. Enterprises should be cautious about sharing access rights on distributed ledgers.
  2. Blockchain transactions are irreversible, therefore every click from an authorized user should be mindful.
  3. 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.

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The Million-Dollar AI Mistake: What 80% of Enterprises Get Wrong

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When we hear million-dollar AI mistakes, the first thought is: What could it be? Was it a massive investment in the wrong technology? Did a critical AI application go up in flames? Or was it an overhyped solution that failed to deliver on its promises? Spoiler alert: it’s often all of these—and more. From overlooked data science issues to misaligned business goals and poorly defined AI projects, failures are a mix of preventable errors.

Remember Blockbuster? They had multiple chances to embrace advanced technology like streaming but stuck to their old model, ignoring the shifting landscape. The result? Netflix became a giant while Blockbuster faded into history. AI failures follow a similar pattern—when businesses fail to adapt their processes, even the most innovative AI tools turn into liabilities. Gartner reports nearly 80% of AI projects fail, costing millions. How do companies, with all their resources and brainpower manage to bungle something as transformative as AI?

1. Investing Without a Clear Goal

Enterprises often treat artificial intelligence as a must-have accessory rather than a strategic tool. “If our competitors have it, we need it too!” they exclaim, rushing into adoption without asking why. The result? Expensive systems that yield no measurable business outcomes. Without aligning AI’s capabilities—like natural language processing or generative AI solutions—with goals such as boosting customer experience or driving operational efficiency, AI becomes just another line item in the budget.

2. Data Woes

AI is only as smart as the data it’s fed. Yet, many enterprises underestimate the importance of clean, structured, and unbiased data. They plug in inconsistent or incomplete data and expect groundbreaking insights. The result? AI models that churn out unreliable or even harmful outcomes.

Case in Point: A faulty ATS filtered for outdated AngularJS skills, rejecting all applicants, including a manager’s fake CV. The error, unnoticed due to blind reliance on AI, cost the HR team their jobs—a stark reminder that human oversight is critical in AI systems.

3. Underestimating the Human Element

AI might be powerful, but it does not replace human judgment.  Whether it’s an AI assistant like Claude AI or OpenAI’s ChatGPT API, Enterprises often overlook the need for human oversight and fail to train employees on how to interact with AI systems. What you get is either blind trust in algorithms or complete resistance from employees, both of which spell trouble.

4. Stuck in Experiment Mode

AI adoption often stagnates when businesses fixate on piloting instead of scaling. Tools like DALL-E or MidJourney may excel in proofs of concept but lack enterprise-wide integration. This leaves companies in an endless cycle of testing AI applications, wasting resources without realizing full-scale business value.

5. Ignoring Change Management

Transitioning to AI technology is as much about organizational culture as it is about deploying AI models. Mismanagement, such as overlooking ethical AI considerations or failing to explain AI’s impact on roles, leads to resistance. Whether it’s a small chatbot AI tool or full-scale AI automation, fostering employee buy-in is critical.

Source: IBM

How to Avoid These Pitfalls

  1. Start with Strategy: Define clear objectives for adopting artificial intelligence programs.
  2. Invest in Data: Build a robust data infrastructure. Clean, unbiased, and relevant data is the foundation of any successful AI initiative.
  3. Prioritize Education and Oversight: Train teams to work with AI and establish clear guidelines for human-AI collaboration.
  4. Think Big, but Scale Smart: Start with pilots but plan to expand AI in finance, healthcare, operations or other areas from day one.
  5. Focus on Change Management: Communicate the value of tools like AI robots or AI-driven insights to teams at all levels.

Graph of AI adoption across different countries

Source:IBM.com

Mantra Labs is Your AI Partner for Success

At Mantra Labs, we don’t just offer AI solutions—we provide a comprehensive, end-to-end strategy to help businesses adopt the complex process of AI implementation. While implementing AI can lead to transformative outcomes, it’s not a one-size-fits-all solution. True success lies in aligning the right technology with your unique business needs, and that’s where we excel. Whether you’re leveraging AI in healthcare with tools like poly AI or exploring AI trading platforms, we craft custom solutions tailored to your needs.

By addressing challenges like biased AI algorithms or misaligned AI strategies, we ensure you sidestep costly pitfalls. Our approach not only simplifies AI adoption but transforms it into a competitive advantage. Ready to avoid the million-dollar mistake and unlock AI’s full potential? Let’s make it happen—together.

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