Training course · Generative AI · Insurance

Generative AI for Insurance Professionals

Vendor-neutral, hands-on day: how language models work, where they go wrong and how to roll out AI successfully in your team.

Duration
1 day (7 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Generative AI with the everyday processes of Insurance. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Generative AI has reached most organisations, yet teams often lack a shared understanding of what language models can actually do and where their limits lie. In this vendor-neutral course you will learn how large language models generate text, why they hallucinate and how careful prompting, context and source checking lead to reliable results. Rather than relying on any provider's marketing claims, you will work with transparent criteria that help you compare tools and judge their fit for your own tasks.

The second focus is everyday implementation. You will identify concrete use cases in your area of work, weigh benefit, risk and effort, and draft a realistic roll-out plan for your team. This covers ground rules for handling confidential data, a practical prompt library and ways to build acceptance and make results measurable. Throughout the day you work with examples from your own field, so you leave with an actionable roadmap rather than a set of abstract ideas.

Insurers process large volumes of applications, claims notifications, expert reports and correspondence every day. Generative AI targets exactly that workload: AI assistants summarise claim files, extract data from loss adjuster reports and invoices, pre-check coverage questions against policy wordings and draft clear customer letters. AI agents and automation speed up intake, inbox classification and broker service. In underwriting, AI helps evaluate risk information and prepare quotes. At the same time, supervisors and customers expect transparency and fairness, especially in claims and pricing decisions. Success comes from using AI to assist claims handlers and underwriters, documenting decisions transparently and giving health and personal data the extra protection it requires.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Claims handlers and benefits specialists
  • Underwriters and risk surveyors
  • Broker and agency service staff
  • Product managers and actuarial teams
  • Process and digital transformation leads

Typical challenges we address

  • Getting to grips quickly with large claim files containing reports, photos and correspondence
  • Checking coverage against complex policy wordings
  • Handling surges in incoming mail, for example after severe weather events
  • Writing customer communication that is clear yet legally sound

What you will be able to do

  • You can explain clearly how language models generate text and why hallucinations occur.
  • You can write prompts that consistently deliver useful, verifiable results.
  • You can compare AI tools against transparent criteria covering data protection, features and cost.
  • You can identify and prioritise concrete use cases for your area of work.
  • Use AI as an assistant in claims, benefits and new business processing with clear decision boundaries.
  • Automate inbox handling and standard correspondence in a traceable way.
  • Take insurance supervision requirements and the protection of health data into account when using AI.

Course outline

Module 1 How large language models work
  • Tokens, context windows and probabilities: what happens when a model generates text
  • Training knowledge versus web search versus your own documents (RAG)
  • Text, images, audio and code: what multimodal models can do today
  • Realistically assessing typical strengths and weaknesses in office work
Module 2 Spotting hallucinations and getting reliable output
  • Why models produce plausible-sounding errors and when the risk increases
  • Prompting techniques: role, context, examples, output format and step-by-step instructions
  • Requesting sources, cross-checking facts and introducing a four-eyes principle
  • Scoping tasks so that AI assists rather than decides
Module 3 Choosing tools without vendor bias
  • Overview of chat assistants, AI built into office suites and specialist tools
  • Criteria: data processing, hosting location, admin features, integrations and pricing model
  • Enterprise licences, API access and locally hosted open-source models compared
  • Building a simple scoring matrix for your own tool selection
Module 4 Identifying use cases in your field
  • Systematically capturing recurring writing, research and analysis tasks
  • Rating benefit, risk and implementation effort for each use case
  • Distinguishing quick wins from strategic initiatives
  • Creating a prioritised use-case backlog for your team
Module 5 Rolling out generative AI in your team
  • Usage rules: which data may go into which tool and who signs off results
  • Building and maintaining a shared prompt library and templates
  • AI champions, training formats and peer exchanges to embed new habits
  • Measuring success: documenting time saved, quality and user satisfaction
Module 6 Practice lab: Insurance
  • Claim file summaries
  • Coverage pre-check
  • Inbox classification
  • Customer letters
  • Underwriting support
  • Broker support
Module 7 Data protection, quality & transfer to daily work
  • Insurers are subject to supervisory rules such as the German VAG, DORA and the GDPR; health data is a special category under Article 9 GDPR and further protected by professional secrecy rules. AI used for risk assessment and pricing in life and health insurance is high-risk under the EU AI Act.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Claim file summaries

An AI assistant turns the first notification of loss, expert reports, invoices and correspondence into a structured summary with a timeline, open points and potential inconsistencies for the claims handler.

Coverage pre-check

AI compares the described incident with the relevant policy clauses, quotes the passages and prepares an assessment that the claims handler evaluates and decides on.

Inbox classification

Incoming emails, letters and forms are sorted by request type, line of business and urgency, policy numbers are recognised and items routed to the right team with a suggested next action.

Customer letters

AI drafts clear letters on claims decisions, requests for documents or policy changes, while keeping mandatory wording and the company's tone of voice.

Underwriting support

For commercial risks, AI summarises risk questionnaires, survey reports and loss histories and compiles follow-up questions and relevant risk features for the underwriting decision.

Broker support

A knowledge assistant answers broker and agent questions about products, underwriting guidelines and processes based on approved documents, always citing the source.

Prerequisites

No technical background is required. It helps to bring along typical tasks from your day-to-day work that you would like to support with AI. You work for an insurer, broker or agency and know the processes in your line of business.

Technical requirements

You will need a laptop with an up-to-date browser and internet access. A free account with a common AI assistant is sufficient; company licences are welcome.

Data protection & compliance

Insurers are subject to supervisory rules such as the German VAG, DORA and the GDPR; health data is a special category under Article 9 GDPR and further protected by professional secrecy rules. AI used for risk assessment and pricing in life and health insurance is high-risk under the EU AI Act.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Tue, 24 November 2026 Online Request
  • Tue, 1 December 2026 Munich Request
  • Fri, 11 December 2026 Online Request
  • Wed, 16 December 2026 Stuttgart Request
  • Tue, 12 January 2027 Online Request
  • Wed, 20 January 2027 Berlin Request

Frequently asked questions

Is the course tied to a specific AI tool?

No. The training is deliberately vendor-neutral. You work with widely used chat assistants and learn principles that apply to all major language models. That way you can transfer your skills to whichever tool your organisation has approved and evaluate new offerings yourself using clear criteria instead of relying on marketing statements.

How is confidential company data handled during the course?

For the exercises you use anonymised or fictitious examples from your area of work. At the same time you learn which categories of data may be entered into which tools, what to look for regarding data processing agreements and use of inputs for training, and how to set out these rules clearly for your team.

Will we leave with something concrete to implement?

Yes. You leave with a prioritised use-case backlog, a scoring matrix for tool selection, tested prompt templates and a draft set of team usage rules. These materials are designed so you can take them straight into internal discussions with management, IT and your data protection officer.

May AI decide on claims?

In practice, AI should prepare decisions, not make them. Automated individual decisions with legal effect are subject to specific requirements under Article 22 GDPR. We show how to design processes so that claims handlers decide on a sound basis and the reasoning is documented transparently.

How should we handle health data?

Health data from health, life or disability insurance needs special protection. Use only approved, contractually secured AI environments, minimise the data you share and pseudonymise consistently. In the course we discuss concrete rules for your processes. Data processing agreements and deletion concepts must also be considered.

How much does “Generative AI for Insurance Professionals” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 1 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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