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.

There was a time when people truly believed that humans only used 10% of their brains, so much so that it fueled Hollywood Movies and self-help personas promising untapped genius. The truth? Neuroscientists have long debunked this myth, proving that nearly all parts of our brain are active, even when we’re at rest. Now, imagine AI doing the same, providing information that is untrue, except unlike us, it doesn’t have a moment of self-doubt. That’s the bizarre and sometimes dangerous world of AI hallucinations.

AI hallucinations aren’t just funny errors; they’re a real and growing issue in AI-generated misinformation. So why do they happen, and how do we build reliable AI systems that don’t confidently mislead us? Let’s dive in.

Why Do AI Hallucinations Happen?

AI hallucinations happen when models generate errors due to incomplete, biased, or conflicting data. Other reasons include:

Human oversight: AI mirrors human biases and errors in training data, leading to AI’s false information
Lack of reasoning: Unlike humans, AI doesn’t “think” critically—it generates predictions based on patterns.

But beyond these, what if AI is too creative for its own good?

‘Creativity Gone Rogue’: When AI’s Imagination Runs Wild

AI doesn’t dream, but sometimes it gets ‘too creative’—spinning plausible-sounding stories that are basically AI-generated fake data with zero factual basis. Take the case of Meta’s Galactica, an AI model designed to generate scientific papers. It confidently fabricated entire studies with fake references, leading Meta to shut it down in three days.

This raises the question: Should AI be designed to be ‘less creative’ when AI trustworthiness matters?

The Overconfidence Problem

Ever heard the phrase, “Be confident, but not overconfident”? AI definitely hasn’t.

AI hallucinations happen because AI lacks self-doubt. When it doesn’t know something, it doesn’t hesitate—it just generates the most statistically probable answer. In one bizarre case, ChatGPT falsely accused a law professor of sexual harassment and even cited fake legal documents as proof.

Take the now-infamous case of Google’s Bard, which confidently claimed that the James Webb Space Telescope took the first-ever image of an exoplanet, a factually incorrect statement that went viral before Google had to step in and correct it.

There are more such multiple instances where AI hallucinations have led to Human hallucinations. Here are a few instances we faced.

When we tried the prompt of “Padmavaat according to the description of Malik Muhammad Jayasi-the writer ”

When we tried the prompt of “monkey to man evolution”

Now, if this is making you question your AI’s ability to get things right, then you should probably start looking have a checklist to check if your AI is reliable.

Before diving into solutions. Question your AI. If it can do these, maybe these will solve a bit of issues:

Can AI recognize its own mistakes?
What would “self-awareness” look like in AI without consciousness?
Are there techniques to make AI second-guess itself?
Can AI “consult an expert” before answering?

That might be just a checklist, but here are the strategies that make AI more reliable:

Strategies for Building Reliable AI

1. Neurosymbolic AI

It is a hybrid approach combining symbolic reasoning (logical rules) with deep learning to improve factual accuracy. IBM is pioneering this approach to build trustworthy AI systems that reason more like humans. For example, RAAPID’s solutions utilize this approach to transform clinical data into compliant, profitable risk adjustment, improving contextual understanding and reducing misdiagnoses.

2. Human-in-the-Loop Verification

Instead of random checks, AI can be trained to request human validation in critical areas. Companies like OpenAI and Google DeepMind are implementing real-time feedback loops where AI flags uncertain responses for review. A notable AI hallucination prevention use case is in medical AI, where human radiologists verify AI-detected anomalies in scans, improving diagnostic accuracy.

3. Truth Scoring Mechanism

IBM’s FactSheets AI assigns credibility scores to AI-generated content, ensuring more fact-based responses. This approach is already being used in financial risk assessment models, where AI outputs are ranked by reliability before human analysts review them.

4. AI ‘Memory’ for Context Awareness

Retrieval-Augmented Generation (RAG) allows AI to access verified sources before responding. This method is already being used by platforms like Bing AI, which cites sources instead of generating standalone answers. In legal tech, RAG-based models ensure AI-generated contracts reference actual legal precedents, reducing AI accuracy problems.

5. Red Teaming & Adversarial Testing

Companies like OpenAI and Google regularly use “red teaming”—pitting AI against expert testers who try to break its logic and expose weaknesses. This helps fine-tune AI models before public release. A practical AI reliability example is cybersecurity AI, where red teams simulate hacking attempts to uncover vulnerabilities before systems go live

The Future: AI That Knows When to Say, “I Don’t Know”

One of the most important steps toward reliable AI is training models to recognize uncertainty. Instead of making up answers, AI should be able to respond with “I’m unsure” or direct users to validated sources. Google DeepMind’s Socratic AI model is experimenting with ways to embed self-doubt into AI.

Conclusion:

AI hallucinations aren’t just quirky mistakes—they’re a major roadblock in creating trustworthy AI systems. By blending techniques like neurosymbolic AI, human-in-the-loop verification, and retrieval-augmented generation, we can push AI toward greater accuracy and reliability.

But here’s the big question: Should AI always strive to be 100% factual, or does some level of ‘creative hallucination’ have its place? After all, some of the best innovations come from thinking outside the box—even if that box is built from AI-generated data and machine learning algorithms.

At Mantra Labs, we specialize in data-driven AI solutions designed to minimize hallucinations and maximize trust. Whether you’re developing AI-powered products or enhancing decision-making with machine learning, our expertise ensures your models provide accurate information, making life easier for humans

Can Distributed Ledgers Accelerate Insurance Workflows?