Artificial intelligence becomes not the affair of the future but the force that promotes change, especially in the insurance industry. Previously involving strict supervision and systematic precaution, the sector currently exploits AI to rationalize costs, enhance accuracy, and improve customer satisfaction. In the case of automated claims processing, advanced fraud detection, or the provision of personalized customer experiences, AI in insurance is giving insurers new meaning to the way they undertake their daily activities.

This session overview of innovative AI use cases in the insurance industry, based on developments across the industry, including several in Germany, and the conditions of success in this technology-driven environment.

Why AI Matters in the Insurance Industry

AI is being used to streamline nearly every aspect of insurance operations. According to a McKinsey study, the industry could generate up to $1.1 trillion in value annually with AI. And yet, despite widespread adoption, with nearly 80% of insurers using AI in some form, many still do not see a return on investment. There’s an extremely important insight that we draw from this: technology alone will not suffice.

Claims Processing: Faster, Smarter, and More Accurate

Claims processing is one of the most time-consuming parts of insurance. Customers often wait days or weeks for decisions and payouts. AI changes that completely.

Real-World Example: Lemonade’s 3-Second Claim Settlement

In 2021, New York-based insurer Lemonade made headlines by settling over a third of its claims in just 3 seconds—no human involvement required. That’s the power of AI in action.

Another large U.S. travel insurer automated 57% of its 400,000 annual claims, slashing processing times from weeks to minutes.

German Innovation: Allianz and Photo-Based Damage Estimation

A German company, Allianz, is said to have developed a tool that uses AI to analyze damage images and estimate repair costs in an instant, referring to millions of past claims. This reduces human error to an almost non-existent possibility, thereby increasing customers’ trust.

Key Benefit: Reduced Claims Leakage

With the help of AI, claims leakage, which consists of unwarranted payments through manual mistakes, can see a decrease by about 30%, translating to bottom-line improvement and freeing human adjusters to work on complicated matters.

Underwriting: Real-Time Risk Assessment

Traditional underwriting relies on historical data and standardized criteria. AI upgrades this to real-time risk modelling using large and diverse data sources like credit scores, telematics, wearables, and social media.

Smarter, More Personalized Insurance Products

AI allows for dynamic pricing based on individual risk profiles. For example:

• Zurich Insurance reported a 90% improvement in risk assessment accuracy after implementing AI tools.
• HUK-COBURG in Germany uses telematics to offer usage-based auto insurance, rewarding safe drivers with lower premiums.

This marks a shift from “one-size-fits-all” to customized insurance policies, which are fairer and more appealing to customers.

Customer Experience: From Reactive to Proactive Service

Artificial intelligence is changing the possibility of insurance providers developing better and more personalized connections with insurance customers. Such examples of AI-based chatbots will provide 24/7 assistance, meaning that the questions related to coverage, claims, renewal opportunities, etc., will be answered instantly.

Personalization at Scale

Using customer data, AI can:

• Send timely renewal reminders
• Recommend tailored insurance products.
• Offer proactive risk tips (e.g., storm alerts)

This kind of hyper-personalized engagement builds loyalty in an industry where over 30% of customers feel dissatisfied, mostly due to slow claims and poor communication.

Case in Point: ERGO Group Germany

Germany’s ERGO Group uses AI to power chatbots and digital assistants, improving customer service and reducing call centre load. They also employ AI to analyze customer behaviour, enabling better product targeting.

Fraud Detection: Stopping Losses Before They Happen

Fraud costs the global insurance industry an estimated $80 billion annually. AI is helping insurers fight back by detecting suspicious patterns in real time.

How It Works

AI algorithms can flag:

• Unusual claim amounts
• Repeated behaviours across different policies
• Voice stress patterns in claim calls

German Example: German Family Insurance

The German insurer uses an AI system for screening health and property claims to spot anomalies that human adjustors would usually miss. Some experts say that much can be done with AI in terms of bringing fraud-related losses down by as much as 40%.

Speed and Flexibility Through Low-Code Platforms

Low-code and no-code platforms allow insurers to develop and deploy AI applications quickly—without waiting for long software development cycles.

Empowering Non-Technical Staff

These platforms enable “citizen developers” (employees without coding backgrounds) to build tools that solve business problems. With built-in compliance and security, they maintain the strict standards required in financial services.

Munich Re’s Example

Germany’s Munich Re uses low-code platforms to accelerate product innovation, rapidly testing new solutions in response to changing market needs or regulatory updates.

Challenges to Overcome: Data Silos, Culture, and Strategy

While AI’s potential is enormous, many insurers struggle with legacy systems, fragmented data, and a resistant culture. For AI to work effectively, it needs access to clean, connected data—which often isn’t the case.

The Human Factor

Success with AI isn’t just about algorithms—it’s about leadership. Insurers need:

• A clear digital strategy
• Executive buy-in and organizational alignment
• Ongoing training to help teams adapt to AI tools

Germany’s Approach: Training and Transformation

Companies like ERGO and Allianz are leading the way in Germany by investing in employee reskilling and creating cross-functional AI teams that include IT, compliance, and customer experience specialists.

The Future of AI in Insurance

The AI in the insurance market is projected to exceed $14 billion by 2034, with early adopters already seeing benefits such as:

• 48% increase in Net Promoter Score (NPS)
• 14% boost in customer retention
• Up to 50% increase in adjuster productivity

But to stay ahead, insurers must move from piloting AI tools to embedding AI across their operations.

Final Thoughts: AI as a Strategic Imperative

AI in insurance is no longer a “nice-to-have”—it’s a strategic imperative. Businesses that use AI in their central operations, culture, and customer experience will drive the next innovation.

It is important to stop seeing AI as a purely technology initiative but as a way of doing things fundamentally differently in the sense of value creation and delivery. The leaders of insurance companies in Germany and other parts of the world know that the question is no longer whether AI will transform the industry but who will destine the sector.

Frequently Asked Questions (FAQ)

What is AI in insurance?

“AI in insurance” refers to the deployment of artificial intelligence technologies for the automation and enhancement of core operations—including underwriting, claims processing, fraud detection, and customer service.

How is AI involved with claims processing?

Using AI, the review of documents, estimates of damages, and even approval of claims have now become automated and processed in minutes or even seconds, as compared to days and even weeks in the past.

Does AI have the ability to pick up insurance fraud?

Indeed. AI tools help prevent and minimize the occurrence of insurance fraud by flagging abnormal trends, voice variation, and data and help uncover potential fraudulent behaviours.

Are there live examples of AI in insurance in Germany?

In Germany, where the insurance market is very well-developed, there is strong evidence of this use: currently, Allianz, HUK-COBURG, ERGO Group, and Munich Re apply AI to enhance the efficiency of operations, improve the customer experience, and prevent fraud.