Training course · Make · Insurance

Make for Insurance Professionals

Automation with Make (formerly Integromat) and AI modules: build scenarios, routers and AI steps while keeping data protection in check.

Duration
2 days (14 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 Make with the everyday processes of Insurance. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Make, formerly known as Integromat, is a visual automation platform that lets you connect applications and automate recurring processes without writing code. Over two days you will learn to build scenarios systematically, from triggers and modules through routers and filters to iterators, aggregators and data stores. You will understand how data flows through a scenario, how to reshape it with functions and how to keep an eye on operations consumption.

The second focus is AI. You connect language models through Make’s AI modules and provider integrations to classify text, extract information from emails and documents, draft replies or enrich content. You learn to catch errors with error handlers, test scenarios and document them clearly. Finally, you address data protection topics such as hosting region, data processing agreements and data minimisation, so that your automations can be run safely within 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 can build Make scenarios independently using triggers, routers, filters and data transformations.
  • You can process lists and records with iterators, aggregators and data stores.
  • You can integrate AI modules for classification, extraction and text generation into your processes.
  • You can safeguard scenarios with error handlers, validation and human approval.
  • 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

Day 1

Module 1 Fundamentals: scenarios, modules and data flow
  • Interface layout, scenario editor and execution history
  • Triggers, actions and searches: module types and their role in a scenario
  • Understanding bundles, mapping and data types and using them deliberately
  • Choosing between schedules, webhooks and instant triggers
Module 2 Logic: routers, filters, iterators and aggregators
  • Using routers and filters for branching and conditional flows
  • Splitting lists with iterators and combining them with aggregators
  • Applying text, date and maths functions to transform data
  • Using data stores and variables for interim values and duplicate checks
Module 3 AI building blocks in Make
  • Connecting language models via AI modules and provider integrations
  • Automating classification, structured data extraction and text generation
  • Building prompts from scenario variables and processing output as JSON
  • Combining AI agents in Make with tools and scenarios as actions
Module 4 Integrations, error handling and testing
  • Connecting common business applications and any API via the HTTP module
  • Applying error handlers such as Resume, Ignore, Break and Rollback
  • Validating AI output and routing unusable results to a person
  • Testing scenarios, optimising operations and documenting them for the team

Day 2

Module 5 Data protection, governance and operations
  • Assessing hosting region, data processing agreements and data flows to AI providers
  • Applying data minimisation and pseudonymisation within scenarios
  • Managing connections, teams and permissions across the organisation
  • Defining approval steps, monitoring and ownership for live operation
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

You should be comfortable with common office and cloud applications and have some experience with AI assistants. No programming skills are required. 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 a Make account; the free plan covers most exercises. API access for a language model is set up together during the course.

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.

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Wed, 9 December 2026 – Thu, 10 December 2026 Bremen Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Online Request
  • Mon, 18 January 2027 – Tue, 19 January 2027 Salzburg Request
  • Tue, 2 February 2027 – Wed, 3 February 2027 Online Request
  • Tue, 9 February 2027 – Wed, 10 February 2027 Kaiserslautern Request

Frequently asked questions

Is Make the same as Integromat?

Yes. Integromat was renamed Make and substantially developed along the way. The core principle of visual scenarios made of connected modules remains, while a redesigned interface, many new integrations and AI building blocks have been added. If you used Integromat in the past, you will quickly feel at home and discover the new capabilities on the course.

How do I keep operation costs under control?

Every module that runs consumes operations or credits, and AI calls also use quota with the respective model provider. On the course you learn to design scenarios so that filters apply early, unnecessary runs are avoided and data is processed in batches. This keeps your automations economical and predictable as volumes grow.

Can Make be used in a GDPR-compliant way?

In principle yes, provided you set up the framework deliberately. This includes choosing the hosting region, signing a data processing agreement, reviewing connected AI services and applying data minimisation. On the course you work through these points with a checklist and learn to filter or pseudonymise sensitive data within scenarios.

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 “Make for Insurance Professionals” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 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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