AI in the insurance sector

What is AI in the insurance sector? / AI, big data – what are they anyway?

Artificial intelligence refers to systems that learn from data, recognise patterns and independently derive decisions or recommendations. In the insurance sector, this means that algorithms analyse policy, claims, customer and risk data in order to automate processes or enable well-founded forecasts.

Big Data refers to large, heterogeneous volumes of data from various sources: for example, from policy management, CRM systems, claims files or external data sources. However, it is not the volume of data that is decisive, but the ability to generate economic value from existing data.

Modern AI systems can consolidate information from various systems and provide context-sensitive answers. This capability is a key driver, particularly within complex insurance structures comprising numerous subsystems.

Areas of application for AI in the insurance sector

AI can be used in almost all core areas of insurance companies:

• Claims management: automated case summaries, fraud pattern recognition, AI-supported initial assessment.
• Underwriting: Risk assessment based on historical and external data.
• Customer service: Chatbots and digital assistants for 24/7 support.
• Sales support: Personalised quotes and product recommendations.
• IT modernisation: Integration or replacement of legacy systems through AI-supported data processing.
• Compliance and reporting: Support with regulatory requirements such as DORA through AI-assisted data classification.

AI assistants really come into their own in administrative processing: they consolidate scattered information, suggest the next steps in the process and reduce the documentation workload.

AI technology used in the insurance industry

From a technological perspective, AI applications in the insurance sector are based on several technological building blocks:

• Machine learning: identifying patterns in claims or risk data.
• Natural Language Processing (NLP): Processing unstructured text, such as in claims reports or emails.
• RAG technology: A combination of knowledge bases and generative AI for context-aware information retrieval.
• Cloud and hybrid architectures: Flexible scaling whilst maintaining control over sensitive data.
• AI-supported data integration: Preparation of legacy data for regulatory reporting.

It is important to note that AI alone is not enough. Integration into existing system landscapes and well-planned change management are crucial.

Benefits for insurance companies and customers

For insurance companies

• Significant time savings on research and documentation
• More consistent process quality
• More efficient fraud detection
• Faster implementation of regulatory requirements
• Future-proof IT architectures

For customers

• Faster claims handling
• 24/7 availability via AI assistants
• Personalised premiums
• Transparent and digital customer journey

Generation Z, in particular, expects mobile, digital and instantly available services. AI is essential for competitiveness in this regard.

Legal framework for AI in the insurance sector

Insurers operate in a highly regulated environment. AI applications must therefore always comply with regulatory requirements:

• DORA (Digital Operational Resilience Act): Sets high standards for ICT risk management, incident reporting and resilience testing.
• GDPR: Regulates data protection and data processing.
• EU Data Act: Establishes new rights regarding data use.
• NIS2: Tightens cyber security requirements.

It is important to note that compliance begins with an understanding of the regulatory requirements, not with the introduction of a tool. AI can provide support, but it is no substitute for expert assessment.

5 Trends in the Insurance Industry

1. Digital sovereignty: Insurers want to retain control over data, technologies and dependencies.
2. Mobile first & Gen Z focus: Digital, instantly available services are the norm.
3. Smart rather than a ‘Big Bang’: IT modernisation is carried out gradually and in a modular manner.
4. AI assistance rather than full automation: AI supports case handlers; it does not replace them.
5. Resilience & cyber security: AI is increasingly being used to detect and defend against cyber risks.

Use Cases: How AI Creates Added Value in Cybersecurity

In the context of DORA and NIS2, AI-enabled cyber security is becoming increasingly important:

• Analysis of incident reports using NLP
• Classification of IT incidents according to regulatory criteria
• Pattern recognition for anomalies
• Support for third-party risk management

AI helps to consistently process large volumes of data from different systems. However, the final assessment remains the responsibility of the IT GRC team.

Artificial Intelligence in Claims Settlement

Claims settlement is one of the most effective areas of application for AI:

• Automated case briefing
• Comparison with similar historical cases
• AI-assisted initial assessment of claim photos
• Documentation support

An AI assistant can summarise the entire case history and provide relevant information in context. This significantly reduces processing times and improves service quality.

Challenges and Outlook

Despite all their potential, many AI projects fail because of:

• Poor data quality
• Unclear business objectives
• A lack of governance
• Lack of acceptance within the organisation

As is evident in practice, the bulk of the effort lies not in the AI model itself, but in data preparation, integration and organisational embedding.

The outlook is nevertheless clear: AI is not a short-term trend, but a structural transformation. Insurance companies that invest today in data strategy, digital sovereignty and intelligent process support will secure sustainable competitive advantages.

The crucial question is no longer whether AI will be used in the insurance sector, but how strategically, responsibly and economically sensibly it will be deployed.