Training course · Generative AI · Finance & Treasury

Generative AI for Finance Teams

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 Finance & Treasury. 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.

Finance and treasury teams handle large volumes of data, tight closing deadlines and strict demands for traceability. Generative AI is reshaping this work: AI assistants summarise bank statements, loan agreements and covenant clauses, draft commentary on cash flow variances and support scenario analysis on interest and exchange rates. AI agents and automation take over recurring steps such as matching payment advices, preparing the liquidity forecast or assembling management reports. What matters is that finance professionals review the output, connect data sources properly and define clear approval workflows. That frees finance teams to focus on analysis, steering and advising management instead of copying figures between spreadsheets, the ERP system and banking portals.

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?

  • CFOs and heads of finance
  • Treasury and cash managers
  • Financial analysts and FP&A teams
  • Corporate finance and M&A specialists
  • Payments and banking operations staff

Typical challenges we address

  • Consolidating the liquidity forecast from many sources such as ERP, bank accounts and business units
  • Explaining and commenting on budget-to-actual variances quickly
  • Reviewing loan agreements, covenants and bank correspondence under time pressure
  • Producing reports for executives, the supervisory board and lenders at short notice

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 assistants effectively for liquidity planning, variance analysis and financing documents.
  • Automate recurring treasury tasks with clear review and approval steps.
  • Critically assess AI output and keep financial data confidential and audit-proof.

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: Finance & Treasury
  • Cash flow commentary
  • Financing agreement analysis
  • Scenarios and sensitivities
  • Management and board reporting
  • Automated remittance matching
  • Policy and process documentation
Module 7 Data protection, quality & transfer to daily work
  • Financial and banking data is confidential and may constitute inside information under MAR; only use approved AI environments with a data processing agreement and GDPR-compliant data handling. Payment approvals must remain under the four-eyes principle and must never be triggered autonomously by AI agents.
  • 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

Cash flow commentary

An AI assistant analyses variances between forecast and actual cash positions and drafts commentary on the main drivers, such as customer receipts, supplier payments or tax dates, which the team then reviews and refines.

Financing agreement analysis

Loan agreements, promissory notes or lease contracts are summarised: maturities, interest mechanics, covenants, termination rights and reporting obligations are extracted into a structured overview for treasury and controlling.

Scenarios and sensitivities

AI helps draft scenario narratives and spreadsheet formulas for interest rate, currency and commodity sensitivities, for example showing how rising reference rates would affect financing costs and covenant ratios.

Management and board reporting

From KPIs, tables and bullet points, an AI assistant produces concise executive summaries, slide drafts and explanatory notes for management, the board or lenders, tailored to audience and level of detail.

Automated remittance matching

An automation workflow reads remittance advices from emails and PDFs, matches them to open items and flags discrepancies for manual review before the data is posted to the ERP or cash management system.

Policy and process documentation

Treasury policies, payment approval processes and control descriptions are structured and harmonised with AI support from notes, existing documents and interviews, making them clear and audit-ready.

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 have hands-on experience in financial planning, treasury or reporting and are confident using spreadsheets.

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

Financial and banking data is confidential and may constitute inside information under MAR; only use approved AI environments with a data processing agreement and GDPR-compliant data handling. Payment approvals must remain under the four-eyes principle and must never be triggered autonomously by AI agents.

Suggested dates

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

  • Fri, 27 November 2026 Online Request
  • Mon, 7 December 2026 Salzburg Request
  • Wed, 16 December 2026 Online Request
  • Thu, 14 January 2027 Bremen Request
  • Thu, 28 January 2027 Online Request
  • Fri, 12 February 2027 Augsburg 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.

Can an AI assistant approve payments on its own?

No. AI can prepare payment proposals, match remittances and flag anomalies, but payment approval stays within your authorisation and four-eyes framework. In the course we show how to design automations so that people remain at the critical control points and every action is logged and traceable for auditors.

How should we handle confidential financial data in AI tools?

Start with an approved enterprise environment that has a contractual data processing agreement and does not use your inputs for model training. Add rules for pseudonymisation, such as masking account numbers or counterparties. We discuss practical guidelines that you can agree directly with IT, data protection and compliance.

How much does “Generative AI for Finance Teams” 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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