Transforming Insurance with Generative AI: A New Era of Efficiency and Personalization

Published on Aug 4, 2023

Generative AI, or generative adversarial networks (GANs), has emerged as a powerful tool in the insurance sector. With its ability to create realistic and synthetic data, generative AI has revolutionized how insurers assess risks, detect fraud, and enhance customer experience.

According to a report by Enterprise Apps Today, the generative AI in the insurance market size is expected to be worth around USD 5543.1 million by 2032. The market sentiment establishes an incline towards adopting the technology into industry practices.

However, while the insurance industry is eager to explore the benefits of generative AI tools, a survey commissioned by InRule Technology reveals that customers may need more time to embrace this technology as part of their insurance experience. The survey found that nearly 59% of respondents distrust or fully distrust generative AI, and 70% still prefer interacting with a human. Insurance companies must carefully consider customer attitudes and readiness when implementing AI technologies.

Let us take a deeper look at how the technology impacts the Insurance industry and how insurers can leverage it.

Applying Generative AI to Insurance

Automation

Generative AI can automate processes by enabling bots to generate contracts and documents.

1. Claims Processing: Generative AI can automate claims processing by analyzing and extracting relevant information from documents such as insurance policies, medical records, and invoices. It can quickly identify the validity of a claim, determine the coverage, and streamline the entire claims process.

2. Underwriting: From analyzing vast amounts of data to assisting insurance underwriters in assessing risks and making informed decisions, generative AI can reduce manual efforts and errors for underwriters. It can automate the evaluation of the applicant’s information, including their medical history, financial status, and other relevant factors, to determine the appropriate insurance coverage and premium.

Accenture has developed an AI platform that can transform claims and underwriting processes by leveraging the massive volumes of data that insurers collect from various sources.

3. Fraud Detection: Generative AI can help insurance companies detect fraudulent claims by analyzing patterns, identifying anomalies, and flagging suspicious activities. It can automate the process of detecting potential fraud, saving time and resources for the insurance company.

4. Customer Support: Generative AI chatbots can be implemented in insurance companies to provide automated customer support. These chatbots can answer frequently asked questions, assist in policy inquiries, and provide personalized recommendations. They can also be programmed to handle simple claim requests, reducing the workload on customer service representatives.

Prominent Insurtech firm Lemonade uses generative AI to power its chatbot, Maya, which can handle the entire insurance process from sign-up to claims. Maya can collect customer information, generate personalized quotes, process payments, and handle claims in minutes. Lemonade claims that its generative AI can reduce fraud and bureaucracy, lower costs, and increase transparency.

Further, Indian Ed-tech platform Sunbird is building its chatbot capabilities using Gen-AI, which helps the bot instantly translate text-to-text, text-to-speech, and speech-to-speech in vernacular languages

By leveraging generative AI for automation, insurance companies can streamline operations, reduce manual work, improve efficiency, and provide a better customer experience.

Predictive Analytics

Generative AI can help insurers predict customer behavior and identify potential risks.

1. Risk Assessment: Analyzing historical data on insurance claims, policyholders, and external factors such as weather patterns and economic indicators to identify patterns and predict future risks. For example, based on past data and trends, it can help insurance companies assess the likelihood of specific claims, such as car accidents or property damage.

2. Pricing Models: Generative AI can analyze data on insurance policies, customer demographics, and other relevant factors to create more accurate pricing models. USA-based management consulting firm Oliver Wyman has developed a Gen-AI platform to help create new products, enhance customer service, provide pricing, and optimize risk management.

3. Fraud Prevention: Generative AI can analyze large volumes of data to detect patterns and anomalies that may indicate fraudulent activity. It can help insurance companies identify potential fraudsters and take preventive measures. For example, it can flag suspicious claims that exhibit unusual patterns or inconsistencies, such as multiple claims for similar incidents or claims with conflicting information.

Improved Customer Experience

Generative AI in insurance can improve customer experience in several ways.

1. Personalized Customer Service: Generative AI can analyze customer data, including interactions with digital platforms and social media, to gain insights into customer behavior and preferences and personalize customer service interactions. For example, if a customer frequently interacts with the insurance company’s mobile app, generative AI can suggest relevant products or services based on their past behavior.

2. Proactive Risk Management: Generative AI can help insurance companies identify potential risks for individual policyholders and take proactive measures to mitigate them. For example, suppose a policyholder lives in an area prone to natural disasters. In that case, generative AI can automatically send personalized safety tips or recommend additional coverage options to protect their property. This proactive approach not only enhances customer experience but also helps prevent losses.

3. Personalized Policy Recommendations: Generative AI can analyze customer data and insurance policies to provide personalized recommendations. For example, if a policyholder’s circumstances change, such as buying a new car or moving to a different location, generative AI can suggest adjustments to their coverage based on their specific needs and risk profile.

Persado is a company that provides a generative AI platform for marketing. Persado’s platform can optimize messages to motivate consumers to engage and act for better messaging results. It can help insurers to personalize their marketing campaigns, increase conversions, and improve customer loyalty.

By leveraging generative AI in these ways, insurance companies in the USA can provide more personalized and efficient customer experiences, ultimately enhancing customer satisfaction and loyalty.

Conclusion

In conclusion, using generative AI in the insurance industry has proven to be a game-changer. With its ability to automate processes, identify potential risks, and create more accurate pricing models, insurers can reduce costs and increase efficiency. Moreover, the technology can also improve customer experience by providing personalized customer service. As such, it is clear that generative AI is a valuable tool that insurers should embrace to stay ahead of the curve and meet the evolving needs of their customers.

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

Transforming Insurance with Generative AI: A New Era of Efficiency and Personalization