Training course – ChatGPT – Insurance

ChatGPT for Insurance Professionals

Use ChatGPT safely and productively at work: projects, custom GPTs, data analysis, Canvas and privacy settings in team workspaces.

Duration
6 hours, one session
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“ChatGPT for Insurance Professionals” is a hands-on course for roles such as Claims handlers and benefits specialists, Underwriters and risk surveyors and Broker and agency service staff. Rather than learning ChatGPT in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Insurance.

For many business teams, ChatGPT is the first AI tool they use every day – yet there is a methodical gap between asking the odd question and achieving real productivity gains. In this hands-on day you learn to apply ChatGPT deliberately to recurring work: drafting and editing text, summarising documents, analysing spreadsheets and structuring decision papers. You work with Projects that keep files and instructions together, with Canvas for reworking longer documents, and with data analysis for Excel and CSV files. Every exercise is based on genuine tasks from your own function.

The second focus is scale and responsibility. You build a custom GPT that knows your templates, tone of voice and review rules, and that colleagues across the workspace can use. We also clarify which data belongs in ChatGPT and which does not, how Team and Enterprise workspaces differ from personal accounts, and which settings matter for model training, memory and sharing. You leave with a personal prompt library, a working GPT and clear ground rules for using ChatGPT responsibly across your organisation.

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 write structured prompts that reliably produce usable results for your day-to-day tasks.
  • You organise your work in Projects and revise longer documents efficiently with Canvas.
  • You analyse spreadsheets and files with data analysis and critically verify the results.
  • You build a custom GPT for a recurring task and share it with your team.
  • 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 Foundations and effective prompting
  • How language models work: strengths, limitations and hallucinations
  • Building effective prompts with role, context, task and output format
  • Refining iteratively instead of starting over: follow-ups and correction loops
  • Choosing models and using web search and deep research appropriately
Module 2 Projects, Canvas and document work
  • Setting up Projects to group files, instructions and chats by topic
  • Summarising, comparing and interrogating long documents
  • Using Canvas to revise text and drafts section by section
  • Templates for emails, reports, minutes and presentation outlines
Module 3 Data analysis with spreadsheets and files
  • Uploading, cleaning and analysing Excel and CSV files
  • Calculating KPIs, replicating pivot logic and explaining variances
  • Generating charts and preparing them for presentations
  • Verifying results: checking calculation steps and sanity checks
Module 4 Custom GPTs for recurring tasks
  • Identifying and scoping good use cases for GPTs
  • Configuring instructions, knowledge files and conversation starters
  • Testing, improving and sharing a GPT within the workspace
  • Maintenance, change tracking for instructions and ownership in the team
Module 5 Privacy, workspaces and governance
  • Personal accounts versus Team and Enterprise workspaces
  • Settings for model training, memory, temporary chats and sharing
  • Handling personal and confidential data in line with GDPR
  • Internal usage policy and quality assurance for AI output
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

Confident use of office applications and a web browser; no prior AI knowledge is required. Feel free to bring your own non-confidential sample tasks from your daily work. You work for an insurer, broker or agency and know the processes in your line of business.

Technical requirements

A laptop with an up-to-date browser and a ChatGPT account; a paid plan or access to your organisation's Team or Enterprise workspace is recommended for custom GPTs, Projects and data analysis.

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.

Dates & locations

For every location we suggest a date roughly four weeks ahead. Your preferred date is always possible.

  • Munich Thu, 10 December 2026 6 hrs Request
  • Salzburg Tue, 15 December 2026 6 hrs Request
  • Berlin Tue, 15 December 2026 6 hrs Request
  • Augsburg Fri, 11 December 2026 6 hrs Request
  • Hanover Fri, 11 December 2026 6 hrs Request
  • Bremen Fri, 11 December 2026 6 hrs Request
  • Stuttgart Mon, 14 December 2026 6 hrs Request
  • Kaiserslautern Tue, 15 December 2026 6 hrs Request
  • Online Thu, 10 December 2026 6 hrs Request

Frequently asked questions

Do I need a paid ChatGPT licence for the course?

You can follow the fundamentals with a free account. However, features such as custom GPTs, Projects with larger files and advanced data analysis are tied to paid plans or come with far more generous limits there. Ideally you join with access to your organisation's Team or Enterprise workspace, so you practise under real conditions.

May I enter confidential company data into ChatGPT?

That depends on your plan, your contractual terms and your internal policy. In Team and Enterprise workspaces, content is not used for training by default, but GDPR and confidentiality obligations still apply. In the course you learn how to classify and anonymise data and how to choose the right settings.

How is a custom GPT different from a saved prompt?

A GPT bundles persistent instructions, knowledge files and optional tools such as web search or data analysis into a dedicated assistant. Colleagues can use it without any prompting skills and get consistent results. A saved prompt, by contrast, has to be copied and supplemented with context every time, which is far more error-prone.

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 “ChatGPT for Insurance Professionals” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 6 hours of live training with a trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. For every location we suggest a date roughly four weeks ahead. In the request form you can always describe your preferred date or period in free text – we check availability and get back to you 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 and Kaiserslautern. We can also run the course exclusively for your team.

Can the course be tailored to our team?

Yes. Tailor-made courses cost approx. €4,500 more, depending on the topic and by arrangement. We agree content, examples and exercises with you in advance.

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