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Technological Revolution Shaping Underwriting in India

The world of underwriting has undergone a significant transformation in recent years. Driven by technological advancements, and changing consumer behaviors underwriting processes have become more efficient and accurate, leading to better outcomes for both insurance companies and customers. One of the recent examples is Munich Re, a leading reinsurer, launching a risk assessment and e-application solution to enable life insurance carriers to underwrite new policies faster and with greater accuracy. Innovative approaches like these are reshaping the insurance industry offering opportunities to enhance customer experience. In this article, we will explore how technology is revolutionizing insurance underwriting in India.

The Importance of Underwriting

Underwriting is not just a process; it’s the backbone of the insurance industry. It’s the mechanism that allows insurance companies to balance risk and reward, ensuring that they remain profitable while providing coverage to their customers.

The Importance of Underwriting

Key factors driving underwriting transformation and its implications on the insurance landscape:

Data Revolution

One of the primary drivers behind the paradigm shift in underwriting is the explosion of data. In today’s digital age, there is an unprecedented amount of data available, including customer demographics, financial history, online behavior, and even IoT-generated data. This wealth of information provides insurers with a more comprehensive view of each individual’s risk profile. By harnessing data analytics and machine learning algorithms, insurance underwriters can analyze this data to make more accurate predictions about an individual’s risk.

Customer-Centricity

In the past, underwriting was often perceived as a one-size-fits-all process. However, the paradigm shift in underwriting places a greater emphasis on customer-centricity. Insurers are tailoring policies and premiums to individual needs and behaviors, fostering customer loyalty and satisfaction. This shift towards personalization not only benefits policyholders but also helps insurers manage risk more effectively.

Mobile Technology:

The widespread use of smartphones in India had a significant impact on underwriting. Insurance companies can leverage mobile technology to collect real-time data, enabling them to make more informed underwriting decisions. For example, health insurance companies can track customers’ fitness levels via mobile apps and offer personalized premiums based on lifestyle choices. This not only benefits the customer but also reduces the risk for the insurance company.

Blockchain Technology:

According to Gartner, the business value generated by blockchain will grow rapidly, touching $176 billion by 2025 and $3.1 trillion by 2030. Blockchain technology can help insurance companies with faster payouts, cost savings, and fraud prevention while improving transparency and efficiency.

Here are some notable use cases that highlight the advancements in insurance underwriting in India:

Automation and AI: Insurance companies are increasingly implementing automated underwriting systems powered by artificial intelligence and machine learning algorithms. These systems analyze vast amounts of data in a fraction of the time and make real-time underwriting decisions, reducing manual intervention and improving the speed and accuracy of the underwriting process. AI algorithms can also continuously learn and adapt, making them more effective at predicting and mitigating risks. For instance, HDFC Life’s InstAInsure uses AI-based automated underwriting to provide instant decisions on insurance applications.

Telematics: Telematics is being utilized by insurance companies to gather real-time data on policyholders’ driving behavior. This data is used for usage-based insurance (UBI) underwriting, where premiums are determined based on an individual’s driving patterns. Companies like Bharti AXA and ICICI Lombard offer telematics-based motor insurance policies in India.

Health Risk Assessment: Insurers are leveraging technology to assess health risks accurately and offer customized health insurance plans. They use wearable devices, mobile applications, and self-assessment tools to collect and analyze policyholders’ health data. Aditya Birla Health Insurance’s Activ Health policy provides personalized wellness solutions and discounts based on policyholders’ health and fitness levels.

Data Analytics for Risk Assessment: Insurance companies are harnessing the power of data analytics to improve risk assessment and offer competitive premiums. By analyzing diverse data sources such as social media, credit scores, and historical claims data, insurers gain insights into customer behavior and risk profiles. This enables them to accurately assess risks and price policies accordingly and offer more personalized coverage options to customers. 

Fraud Detection and Prevention: Advanced analytics and machine learning algorithms are being employed to detect and prevent insurance fraud in underwriting. By analyzing patterns, anomalies, and historical data, insurers can identify fraudulent claims and mitigate risks to ensure their long-term profitability proactively. This helps maintain a healthy insurance ecosystem and reduces fraudulent activities.

Conclusion

Technology has undoubtedly transformed the underwriting and risk assessment processes in the Indian insurance industry. Insurance companies can now offer more personalized and efficient services to their customers and enhance efficiency, accuracy, and customer experiences in underwriting processes. This not only benefits the insurance industry but also contributes to the overall growth of the Indian economy.

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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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