How generative AI can transform the insurance industry

The insurance sector is highly data-driven and characterized by complex, often unstructured information flows. This is precisely where Generative AI excels. Solutions like Mappr make it possible to structure these data streams efficiently and automatically extract relevant information. In this way, extensive documents such as damage reports or medical records can be analyzed in a short time and transformed into actionable insights. For example, insurers can use AI models (e.g., Large Language Models) to assess risks more accurately while creating more precisely tailored offerings. A survey conducted by EY-Parthenon shows that 82% of insurers cite productivity gains and cost reductions as the main reasons for implementing Generative AI

Generative AI is used in numerous areas of the insurance sector and opens up new perspectives for business processes.

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Claims processing: Through the use of AI-based chatbots, initial claims can be recorded efficiently, while providing real-time information on repair services. This not only increases customer satisfaction but also significantly shortens processing times. (Source: McKinsey)

Underwriting: Generative AI particularly supports insurers in the underwriting process by combining historical claims data, external sources (e.g., weather reports, demographic data), and current policies. This allows risks to be assessed more accurately and more individualized offers to be developed. The result is optimized pricing with a simultaneous reduction in processing time. (Source: Deloitte)

Personalization: One of the main advantages of GenAI lies in its ability to customize insurance products and tariffs for individual customers. By analyzing data on preferences, behaviors, and needs, tailored solutions can be developed that are better aligned with specific requirements. (Source: Deloitte)

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Selecting a suitable GenAI use case is crucial for successfully integrating the technology and creating real added value. A tried-and-tested approach consists of several steps:

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Step 1: Define the Focus Area

First, the specific area of application within the insurance company is determined—for example, a business unit such as claims settlement, underwriting, or customer service. Alternatively, a particular location or a process within the value chain can be selected. It is wise to choose areas where employees are already open to innovation or where there is particularly strong expertise. This way, potential can be identified and implemented more quickly.

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Step 2: Analyze the Current State

Following the principles of Lean Management, the current state of the selected process is thoroughly examined, for example by means of value stream analyses. The focus lies on the following aspects:

• Time required per process step (e.g., manual checks in claims processing)
• Costs per process step
• Data availability (high, medium, or low)

Process segments that have both high time and cost expenditures as well as good data availability are especially suited for in-depth analyses. A classic example is the automated recording of claims or the implementation of AI-based fraud prevention systems.

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Step 3: Market Research and Ideation

Once a relevant use case has been identified, the search for the optimal AI solution begins. It is often advisable to conduct interdisciplinary innovation workshops to bring together different perspectives. In advance, it should be clear whether the insurance company wants to develop a solution itself ("build") or buy external solutions ("buy"). Based on the existing internal competencies and available market offerings, a decision is made as to which path is most promising.

In these workshops, creativity techniques (e.g., brainstorming, design thinking) are used together with experts, employees from various departments, decision-makers, and external partners to develop and classify concrete use cases. These use cases are then plotted on an "opportunity radar." Examples of promising use cases include predictive risk assessments, automated claims processing, and the use of AI-supported tools in sales.

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Step 4: Prioritize Use Cases

Finally, the developed use cases are ranked by relevant criteria, for example using an impact-effort matrix. The focus is on:

• Effort (time needed, technological complexity)
• Impact (financial potential, improvements in customer service, reduced regulatory risks, or faster processing times)

By using this systematic approach, insurers ensure that they select from the multitude of possible AI projects those that unfold the greatest benefits while still fulfilling important regulatory requirements and security aspects.

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Despite the many advantages of Generative AI, insurers should not underestimate potential risks. These include:

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Regulatory and ethical requirements: The EU AI Act imposes strict requirements concerning data protection, the handling of biases (bias management), and the traceability of algorithmic decisions. Insurers need clear mechanisms to ensure compliance with these regulations. (Source: McKinsey)

Fraud potential: If GenAI models are not adequately protected or trained, there is a risk that manipulative external interventions or faulty data processing will be facilitated. Insufficiently validated models could produce incorrect forecasts and make fraudulent activities easier. (Source: McKinsey)

Organizational hurdles: In addition to technical and regulatory factors, employee acceptance plays a key role. Resistance or insufficient training can significantly impair the success of a GenAI project. Therefore, it is essential to establish change management programs and provide targeted training so that employees are empowered and motivated to use the new technology. (Source: McKinsey)

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A comprehensive framework that includes ethical AI development, continuous review, and compliance is therefore indispensable for building trust and minimizing risks, (Source: Deloitte)