Training course · Perplexity · Insurance

Perplexity for Insurance Professionals

AI-powered research with Perplexity: cited answers, source control, Spaces, Deep Research and systematic fact-checking.

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

“Perplexity 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 Perplexity in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Insurance.

Perplexity combines language models with live web search and delivers answers backed by numbered citations. That makes it particularly suited to work where currency and traceability matter: market and competitor monitoring, research into regulation and standards, supplier and partner due diligence, or preparing decision papers. In this practical day you learn to phrase research questions precisely, steer the choice of sources deliberately and use follow-up questions to turn an initial answer into a robust research result.

You work with Spaces, where you group research on a topic, add your own files and set instructions for the team. With Deep Research you produce extensive reports that evaluate many sources and bring them together in a structured way. Quality is a key focus: you learn to assess sources, cross-check claims and recognise typical failure patterns such as outdated or misattributed information. We also address data protection and licensing questions for business use. You leave with a reusable research process tailored to your own function.

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 phrase precise research questions and steer the choice of sources deliberately.
  • You assess sources by origin, currency and reliability and trace claims back to the original.
  • You organise research in Spaces and combine web sources with your own documents.
  • You use Deep Research to produce structured reports and decision support.
  • 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 of AI-powered research
  • How it works: language model, live web search and citations
  • How it differs from a classic search engine and a general chat assistant
  • Framing research questions precisely: time frame, region, source type
  • Follow-ups and drilling down instead of one-off queries
Module 2 Steering and assessing sources
  • Narrowing sources: web, academic literature, your own files
  • Targeting primary sources, official websites and specialist publications
  • Assessing source quality: origin, currency, vested interests
  • Tracing quotes and figures back to the original source
Module 3 Spaces and teamwork
  • Setting up Spaces for topics, projects or client requests
  • Uploading your own documents and combining them with web sources
  • Storing instructions for consistent output formats
  • Sharing research and turning it into pages or reports
Module 4 Deep Research and structured reports
  • Tackling broad questions with Deep Research
  • Producing market overviews, competitor comparisons and topic briefs
  • Turning results into tables, side-by-side comparisons and executive summaries
  • Recognising the limits of in-depth research and refining results
Module 5 Fact-checking, data protection and workflow
  • Systematic fact-checking: counter-research and spotting contradictions
  • Typical failure patterns: outdated, misattributed or fabricated details
  • Data protection, enterprise licences and handling confidential queries
  • A reusable research process and source documentation
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

Experience with online research in a professional context; no prior AI knowledge is required. 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 Perplexity account; we recommend Pro or Enterprise access to use Spaces with files and Deep Research in full.

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.

  • Fri, 27 November 2026 Online Request
  • Wed, 9 December 2026 Bremen Request
  • Tue, 12 January 2027 Online Request
  • Wed, 27 January 2027 Salzburg Request
  • Tue, 9 February 2027 Online Request
  • Thu, 25 February 2027 Kaiserslautern Request

Frequently asked questions

How does Perplexity differ from ChatGPT or a search engine?

A search engine returns a list of links, and a general chat assistant often answers from its training data. Perplexity searches the web at the moment you ask, summarises what it finds and backs each statement with citations. That makes it particularly well suited to current, verifiable research – although you still need to assess the sources yourself.

Can I rely on Perplexity's citations?

Citations make verification much easier, but they do not replace it. Occasionally a statement is attributed to a source that does not say quite that, or an outdated page is used. In the course you learn a lean fact-checking routine that lets you quickly verify critical statements against the original source.

Is Perplexity suitable for confidential research within a company?

Enterprise plans offer additional commitments on data use and administration. Even so, you should not put trade secrets or personal data into queries unless your internal policy explicitly allows it. We discuss how to phrase queries so that sensitive details stay protected while the research remains useful.

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 “Perplexity 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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