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The Cognitive Cloud Insurer is Next

4 minutes, 8 seconds read

Today’s Insurance enterprise is moving away from the all-too-familiar ‘reactive-only’ approach to a new predictive-first model. The sector is seeing dramatic changes, as we enter the fourth Industrial Revolution (Industry 4.0) — or The Connected Age. Digital businesses are gradually realizing the limitations of human and machine systems without any real intelligence or computing power behind it. Between human prone errors and the scalability challenges of traditional technologies — a new mechanism is required to learn and adapt better. 

Enter Cognitive Computing. But what is it?

The short answer is — it has everything to do with interpreting data. Big Data, to be precise. This activity is particularly hard because most of the data in use remains unstructured. In insurance, for example, nearly 90% of carrier data is disparate or partially structured as text & image data, in varying formats. With cognitive computing, data can be made meaningful and then used to derive new insights for future use.


To achieve this, ‘Cognitive Systems’ leverage the use of distinct technologies such as natural language processing, machine learning and automated reasoning. It helps in processing great volumes of complex data and can aid faster & accurate decision-making by breaking down the complexities in big data. When done right, a cognitive computing system can comprehend, reason, learn and interact with humans naturally ultimately enhancing the enterprise’s digital intelligence capabilities.

Another aspect of cognitive computing is the ‘Cloud’ advantage. Cloud computing is not new, however, when fitted with a cognitive solution — it can foster dramatic agility to organizational workflows. 

For the digital insurer, this means that all aspects of the value chain can be transformed, ushering in a new business model that seamlessly engages with both customers and prospects in near-real-time, at all times. 

Also read – How does XaaS help your business?

The Cognitive Insurance Transformation Journey

Transitioning from a digital to a cognitive business enabled by the ‘cloud’ has a clear business objective behind it — evolve the model to improve profitability. The addition of the cognitive component allows smart systems to free up critical manned resources and drives greater (STP) straight-through processing. 

Take ‘underwriting’ for example, which is an area of insurance that necessitates looking at  vast heaps of unstructured data. Without the supporting information, the risk cannot be precisely measured or priced. 

Accelerating data analysis from historical information can improve the underwriter’s efficiency in the manufacture of meaningful and personalised insurance products, within short turn-around time. This is how insurance carriers will stay their competitive advantage when vying for the wallet-share and mind-share of tomorrow’s customer.

The Cognitive Insurer in cloud is Next

Source: The Cognitive Insurance Value Chain

Yet, the redesign of the underwriting process is only one of many insurance processes that has the potential for Cognitive enhancement. The number of connected things will grow to 25 billion by 2021, which will increase the amount of data. Insurance data alone is expected to grow by 94%. Other parts of the value chain like claims processing, new business and underwriting, rapid customer onboarding, rules-based processes and contract validation are also experiencing cognitive upgradation.

In the past few years, the number of cognitive projects in insurance is on the rise. Carriers are running pilots, testing and validating the right use cases to invest in. For instance, Australian Insurer, Suncorp used IBM’s Watson for ratifying a specific use case — determining who is liable for causing a motor accident, by studying 15,000 historical records of de-personalised claim files.

The Cognitive Insurance process and application

Source: CognitiveScale

Intelligent and cognitive systems like these can do a lot more. From cognitive claims to cognitive chatbots — AI and Machine Learning are behind new behaviour-based, pay-as-you-use products in insurance. Automated post-hospitalisation claims, Motor damage estimation using advanced image recognition, Cognitive mail handling through intention analysis, etc. among others are just a few examples of AI solutions being deployed by Insurers, who are evolving their business models along their transformation journey.

Our own SaaS-based intelligent platform built for improving insurer workflows, FlowMagic takes advantage of cloud-based capabilities to enhance business automation. The intuitive Visual Platform uses AI-powered applications that are easily configurable requiring zero-coding effort, while the jobs can be visually monitored continuously to give real-time decision-ready insights.

Cognitive-Insurance-Ecosystem-Flowmagic

FlowMagic — Visual AI Platform for Insurer Workflows

Here’s a simple 3 step formula for a successful cognitive cloud transformation journey:


1. Identify (internally) use cases with a potential for a high degree of market disruption.

2. Validate (both internally & externally) the use cases through small-scale pilot deployments.

3. Define areas in your operational value chain ripe for transformation, that will enable new processes, engagements and business models through it.

By 2020, 25% of customer service and support operations will integrate with cognitive cloud-enabled chatbots to deliver natural, conversational guidance to users. Solutions like these have proven demonstrable ROI in both front & back-office operations, creating over 80% FTE savings for the enterprise.

Mantra Labs is an InsurTech100 company, that helps digital insurance enterprises enhance agility and operational efficiency through new Cognitive Cloud capabilities. To know how, reach out to us at hello@mantralabsglobal.com

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Machines That Make Up Facts? Stopping AI Hallucinations with Reliable Systems

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

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