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How Mobile Micro-Health Insurance can unlock ‘Digital for Bharat’?

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4 minutes, 8 seconds read

Mobile-enabled micro-health insurance is escalating at a good rate with advancement of digital healthcare technology. It has the potential to deliver quality healthcare services to people by improving accessibility and keeping people well-informed about health issues, thus reducing out-of-pocket expenses. Consumers are prioritizing health above other needs as the rise of digital services in India has enabled catering to the at-home population In India.

Keeping Customers Engaged using digital health tools

Practice of healthcare through mobile can be made interactive by integrating services that can cater to customer needs:

  1. Using chatbots to help customers settle health related queries and diagnosis through simple question-answer sessions. Health emergencies can be solved any time with chatbots due its 24/7 availability. Max Life insurance has made it easier for customers to avail customer service through max life assistant Mili that is integrated in Whatsapp.
  2. Use of mobile health apps helps customers to receive personalized service. Mobile health apps provide virtual care, health tips, and keep track of health status, and locate nearby hospitals. TATA AIA life insurance company partnered with Practo to gain access to a digital health platform through which customers can book appointments, order medicines and consult doctors online.
  3. Integration of mobile apps with fitness trackers, smart health watches helps customers to receive daily updates on their health & well-being. Max Bupa Health insurance partnered with GOQii to track customers’ health and offer discounts to those who achieved healthier goals and lifestyles. 
  4. Use of mobile payments such as mobile wallets, NFC can help customers pay premiums with just a few taps. Reliance general insurance partnered with Paytm and launched “COVID-19 benefit insurance policy” that covers quarantine and health treatment expenses for COVID-19 patients.

More than 2.4 billion people worldwide live on US$2 or less per day. Most low-income families will see their savings be completely wiped out owing to higher out-of pocket healthcare expenses and are likely to be pushed further into poverty. Below are a few mobile micro-health insurance products that are helping such low-income families cover health risks with minimal costs at difficult times.

Innovative New products in micro-health insurance:

  1. BIMA Health- following a mobile insurance model and having partnered with several mobile operators, BIMA covers short-term health events for low-income families by providing tele-doctor services, free health programs giving health tips through SMS, appointment booking services wherein the micro-payments are deducted from monthly phone bills.  
  2. Pona na Tigo Bima- MicroEnsure partnered with Tigo, Bima and Golden Crescent and developed a health insurance product “Get Well with Tigo Insurance” that provides life and hospitalization insurance covering 30 nights in a hospital and uses mobile money for claim settlements. 
  3. Y’ello Health- this micro-insurance service established by MTN Nigeria provides health insurance cover to Nigerians where they can pay and have access to medical treatments through mobile phones. People have access to around 6000 hospitals across the country that are registered in NHIS.
  4. Kilimo Salama: operated by safaricom, Syngenta foundation and UAP insurance, the insurance scheme allows Kenyan farmers to insure farm equipment and inputs against drought and heavy rain. It offers “pay as you plant” insurance by syncing mobile payments and solar powered weather stations. A farmer pays 5% extra for farm inputs for climate coverage. When a weather station reports extreme climate change, the farmer registered with that station automatically receives the amount in mobile. 

MNOs have been the major drivers to enhance the microinsurance industry. Mobile being the dominant in healthcare technology, can be used to structure niche insurance products and serve to educate people on various health issues. Mobile micro-health insurance can serve as a protective blanket against health emergencies as mobile can bridge the gap between the insurers and low-income families, be it mobile policy information, claims filing, renewals, query and claim payments. An adequate balance can be achieved between affordability and accessibility by partnerships with MNOs to deliver real value to the customers.

Untapped Opportunity & Drivers of Micro-health Insurance

In developing countries, the estimated volume for microinsurance is between 1.5 and 3 billion policies. These policies typically account for demand in health, agriculture, property, and disaster cover. At present, only 5% of this market is currently tapped and is being driven by large commercial insurers. To expand the market, commercial insurers should partner with innovative startups, NGOs and other facilitators. As mobile penetration deepens, it will also open more doors for low income groups to have access to better quality financial savings products. For instance, WhatsApp which has a total of 400M users in India, 15 million of which are small businesses, is targeting financial services such as insurance, micro-credit & pension for the rural/informal sector through ‘WhatsApp Pay’. The ‘Digital for Bharat’ challenge needs simplicity in the products & services being designed for the rural mass and finding innovative distribution channels to truly establish the roots of this market.

To know about how healthcare industry is bringing hospitals to a customer’s doorstep, watch our webinar on Digital Health Beyond COVID-19.

Further Readings:

  1. Reimagining Medical Diagnosis with Chatbots
  2. HealthTech 101: How are Healthcare Technologies Reinventing Patient Care
  3. What will be the state of the healthcare industry post pandemic?
  4. Healthcare Chatbots: Innovative, Efficient, and Low-cost Care
  5. Does Microinsurance work for India’s poor?
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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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