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What can APAC insurers learn from each other in an After-COVID World

The Pandemic has forced Insurance carriers, both legacy and new, to adapt their business models, re-evaluate risk modelling and pricing strategies and conceive fresh ways to interact/engage with prospective buyers. Especially within the APAC region, there are many businesses that have adapted to the new business normal successfully and carved out specialised tactics to thrive. 

In this blog we will take a look at some instances that reflect these changes and the measures taken by Insurers that will provide insights for other carriers. But before hopping into those points, let’s have a look at few trends which are worth mentioning:

Changing Trends To Be Considered for a Longer-Term

  1. More Consumer-Centric Solutions:

Most APAC insurance companies use a one-size-fits-all approach; wherein, offering similar packages or products to a broader audience. But that is not the case anymore. In times like this, customers are picky and have become more aware than ever and expect solutions customized for their requirements. 

Therefore, to meet their expectations, many APAC insurers started offering tailor-made policies that meet individual requirements. This is one of the trends that we will see going forward on a longer-term.

  1. Enhanced Claim Settling:

Along with companies focusing on providing customer-centric solutions, they’ve also been focusing on enhanced claim settlement mechanisms. This will aid customers to get financial backup under challenging times.

Now, claims can be raised faster, and policyholders can simply upload the documents required. Insurers can use this to increase their efficiency and settle claims faster and with more efficiency.

  1. Digital Operations:

Since the government has alerted people to follow the guidelines and maintain social distancing, people were rarely stepping out. And this gave rise to more online transactions and deals made online. An increasing amount of people are buying things online, and that goes for insurance as well. 

More number of APAC Insurers started offering insurance online and made other processes feasible online as well. This empowers policyholders to make their payments online and upload their documents from their own homes’ safety and comfort.

Essential Learning for APAC Insurers to Adapt Quality Change

The unappreciated effects of the pandemic have shaken the whole economy and businesses on a large scale. Indeed, all the business models have experienced its effects, but for some, it went positive, and for others, it was negative. 

However, the insurance sector stands in the middle of the ground. Under this umbrella, businesses have used various tactics to cope with the negative aspects and paved their way from surviving to thriving.

To win more customers in the post-covid world, a proper action plan is required. The following points are what successful APAC Insurers are up to; you can use them as inspiration to power-pack your business for post-covid scenarios. 

  1. Telemedicine in Health Plans

Telemedicine is the distribution of clinical and health-related services remotely in real-time two-way communication. This concept had just begun to grow in India but COVID catalyzed the process as the nation went under lockdown and social distancing became the norm.

Since telemedicine became widely popular in the post-COVID world, the IRDAI instructed insurance companies to cover the medical costs incurred on telemedicine as well if their health plans offer coverage for doctor’s consultations. Therefore, health plans are now more inclusive in the Post-Covid world as they cover telemedicine costs too.

APAC insurers can adapt to this concept as it will power-pack their health plans even more. 

  1. Replacing Physical Signatures

Because of social distancing norms, many insurance industries have adopted the elimination of physical signatures on proposal forms. This can be acquired by APAC insurers for the long term. 

Now, individuals are liberated to purchase insurance plans with online proposals which are verified by the confirmation mails or OTPs rather than physical signatures of policyholders. This also saves individuals to travel to the workplace and save a lot of time. 

  1. Implementing Virtualised Outreach

Being the new normal, remote working has made us comfortable with having virtual meetups and conferences. Many companies in Asia-Pacific have adopted this method in outreaching and having a potential conversation with new customers. It enables them to get their work done by sitting in their comfort zones. This is a good adaptation in the long run and saves customers from traveling to a location. 

Therefore, make sure you are active in this digital world to remain visible to your clients. Utilize various ways to keep them warm. It may be email newsletters, videos, social media, and even interactive webinars so that your business remains at the forefront of your clients’ minds.

  1. Adoption of AI-driven systems

Acquiring the power of AI is not a business decision anymore, it has become a survival strategy. And Covid-19 has helped more APAC insurers to understand this. Therefore, the insurance industry is undergoing a swift and tremendous transformation, driven by the burning need to improve customer experience and smooth interaction.

AI can majorly help APAC insurers in the following ways:

  • Managing risk easily and efficiently with the help of neural networks. It can detect red flag fraud patterns and minimize fraudulent claims
  • Makes the agent-customer interaction better and smooth 
  • It helps in making the claim process easy and fast by eliminating the manual efforts from document processing to fraud flagging. 
  • Liberates APAC insurers with 24/7 customer service. 
  • AI can help insurance companies in determining business-critical aspects appropriately such as the maximum possible loss, probability, and pricing more.
  • Efficiently recommends the most beneficial products to their customers based on previous behaviors.

Wrapping Up

These lessons have been gathered through analysing and studying the business impact of responding to economic, political, and public health crises in the region and the global insurance industry at large. Coping up with the pandemic is a significant achievement in itself. But to grow sustainably through a Global crisis takes significant planning, effort and resources to quell the tide. APAC carriers will have to adopt fast, be nimble and navigate swifty for the uncertain road ahead in an After-COVID World. 

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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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