AI Training · Finance

AI Training for Finance

Credit memos, board packs, market notes, contracts, and client letters: a finance team's actual output is almost all text, which happens to be exactly what a language model drafts and reads fastest. This program turns that speed into a safe habit, with a person checking every number before anything goes out the door.

modules
6
hours
13
Contact us

Why this training

Why AI in this industry

Credit memos, board packs, market commentary, contracts, statements, and regulatory updates: that's most of what moves through a finance team in a given week, and almost none of it is arithmetic. It's reading and writing. A language model does both fast, sometimes getting the answer wrong in a way that looks completely right. A plausible number invented for a memo. A contract clause misread with total confidence. A client's details pasted into somebody's personal ChatGPT account, because the deadline landed that afternoon. None of it looks like a mistake until a person checks it against the source.

That combination, high volume, repetitive structure, and a low tolerance for a wrong number, is what makes finance a good match for this kind of tool, and exactly why using it without training is risky. A model turns out a first draft in a fraction of the time a person would need, and it stays consistent on tone in a way a tired analyst at 6pm often isn't. It doesn't know which number in front of it is real. Someone still has to check, every time.

This program builds around that split for two days. Teams practice drafting and extraction in ChatGPT, Microsoft Copilot, and Gemini, alongside a verify-against-source habit that never goes away. They also learn the GDPR and KVKK rules for what can and can't enter an AI tool. One line doesn't move in any module. Decisions about credit and investment stay with the accountable person. The same holds for a client relationship. Every syllabus on this page starts as a draft. A discovery call before the training reshapes it around your institution's own documents and systems. The industry programme list shows the rest.

The reporting calendar owns the week before the analysis does

Credit memos, market commentary, and board packs do not write themselves, and in most finance teams they eat more hours than the analysis behind them. A model can turn out a workable first draft before the coffee's cold, but someone still has to stand behind what it says.

up to 40%hours given back on routine drafting, per industry pilot data

A contract's forty pages, read end to end, every time

Contracts, statements, and filings repeat the same structure for page after page, and reading them line by line is most of what document review means in practice. A model pulls the dates and terms, including obligations, out in seconds. Somebody still checks each one against the actual page, because a wrong term missed once can cost more than the hours saved finding it.

The same client answer, retyped from memory five times a day

Relationship managers field near-identical questions from different clients, and the wording drifts a little every time someone rewrites the answer from scratch. AI keeps the tone and the facts consistent in minutes. Deciding what goes to that particular client still sits with the person who knows the account.

Staff are already pasting client data into ChatGPT on their own accounts

Unapproved, unlogged use happens most weeks with information that should never leave the institution, so training replaces it with rules, a paper trail, and a named owner.

A subscription doesn't teach anyone what to trust

Excel, Word, Outlook, and the browser already carry AI features for most of your staff. Buying another tool won't teach anyone which draft to trust on sight or which one needs a full rewrite before it goes anywhere. That only comes from practice.

Syllabus

Training syllabus

  1. Where generative AI helps a finance desk

    120 minBeginner

    What language models draft well and where they invent things confidently. A regulated institution raises the stakes on both. Front- and middle-office staff join their back-office colleagues and leave this session with the same basic picture of the tool before anyone touches a live workflow.

    • What a language model is doing, mechanically, when it writes a sentence
    • Hallucination in financial text: a confident, invented figure that reads as fact
    • Where AI already sits inside Excel, Word, Outlook, and the browser
    • Which tasks to hand to the model and which decisions stay with people
    • Picking a first workflow small enough to fail safely

  2. Prompt craft for the documents your desk produces

    120 minBeginner

    A repeatable structure, role, context, task, format, and checks, practiced directly on your own memos, letters, and reports. Everyone leaves with prompts they'll reuse next week.

    • A reusable prompt template built for financial documents
    • Giving the model context without client names or confidential figures
    • Rewriting a draft twice before accepting the first answer
    • Setting tone and structure for a committee and client, then an auditor
    • Building the first entries in a shared prompt library

  3. Turning memos and reports into faster filing drafts

    180 minIntermediate

    Credit memos, portfolio summaries, board reporting, and contract review make up most of a finance team's writing. Each one now runs through the same AI-assisted drafting and extraction routine. A person checks the draft against the source before it moves forward, every time.

    • Drafting credit memos and analysis notes from structured inputs
    • Writing market and portfolio summaries that cite where each figure came from
    • Pulling terms, dates, and obligations out of contracts and statements, then verifying them
    • Turning a long regulatory update into a short internal briefing
    • Getting AI to explain a spreadsheet formula or draft a scenario narrative
    • Catching an invented number before it leaves the draft folder

  4. Client letters and calls, plus everything between

    150 minIntermediate

    Emails, letters, meeting briefs, and product explanations at the volume a relationship team handles. Plus a straight answer on where AI can help with advisory work, and where it must stay out of the decision entirely.

    • Client emails and letters pitched at the right tone and formality
    • Answering the same questions
    • Meeting-prep briefs and call summaries, written from notes
    • Explaining a product change or a portfolio move in plain language
    • Where AI can help with advisory work, and where it must not weigh in
    • The point where advisory support ends and a person has to decide

  5. Confidential data, GDPR, KVKK, and staying auditable

    120 minIntermediate

    What counts as client or confidential information, including inside information, what that means under GDPR and KVKK, and how to keep a paper trail so model risk stays visible.

    • What counts as client and confidential information, including inside information
    • Consumer chatbots versus enterprise tools, and where each one sends your data
    • Handling personal data under GDPR and KVKK once AI is in the workflow
    • Every AI output, unverified
    • Writing an AI usage policy a regulated institution can enforce and audit
    • Logging AI-assisted work so it can be reviewed later

  6. Getting from trained individuals to an institution that uses AI well

    90 minAdvanced

    Pilots, champions, and a measurement rhythm that keeps compliance involved once the trainers are gone, so the habits from the room are still there on a normal Tuesday three months later.

    • Picking a first pilot workflow with a time saving you can measure
    • Naming champions across front and middle offices, with the back office included
    • Bringing compliance in early
    • Measuring adoption by what people actually do with it
    • A written 90-day plan before the training ends

Outcomes

Outcomes & audience

What you will learn

  • A credit memo drafted before the coffee's cold, checked before it ships
  • Build a reusable prompt library
  • Pull the dates and terms from a contract, then verify them against the source
  • Keep client letters on-tone
  • Handle client data the way GDPR and KVKK require
  • Spot a hallucinated figure before it reaches a client or a committee
  • Take a pilot workflow to a rollout compliance signs off on

Who should attend

  • Analysts and reporting teams who draft the memos and the packs
  • Relationship managers and client advisory staff
  • Credit and risk teams, including compliance staff
  • Corporate finance and treasury teams
  • Operations and middle-office staff
  • Fintech product and customer-experience teams

Format

Training format

The core runs two days, usually split into half-day blocks around reporting cycles and market hours. Onsite and live-online formats cover the same syllabus.

Format
Onsite or live online
Duration
2 days (about 13 hours, can be split into half-day sessions)
Group size
Up to 20 participants per group
Language
English or Turkish
Materials
Prompt library, document templates, and exercise workbook
Certificate
Certificate of completion

About Zeo

Why Zeo

Zeo started in 2011 and now works out of San Francisco, Istanbul, Ankara, and Lisbon. We run Copilot Academy and organize Digitalzone, an international digital marketing conference. This program draws on the 10+ years of consulting and training work behind that, applied to corporate AI adoption.

  • 2011founded in Istanbul
  • 10+years of consulting and training experience
  • 3offices: San Francisco, Istanbul, Ankara, Lisbon

Keep exploring

AI for FP&A & Financial Analysts6 modules · 12 hoursAI training for FP&A and financial analysts, built around the planning calendar: driver trees, forecast assumptions, variance commentary, and board-ready packs, with every figure still reconciled to governed data.View trainingAI in Accounting & Audit6 modules · 13 hoursAI training for accounting firms, audit teams, and in-house finance departments, built around working papers and reconciliations: faster drafts, every number checked against the ledger, and the reviewing professional still the one who signs.View trainingAI Training for Insurance6 modules · 13 hoursZeo's AI training for insurers, brokers, and agencies: claims correspondence and policy documents drafted faster, long files read and checked in minutes, with a person deciding every claim and every coverage call.View trainingAI in Data & BI Analysis6 modules · 12 hoursSQL query drafting, dashboard narratives, data documentation, and stakeholder reporting, taught to data and BI analysts with every number checked against source before it ships.View training

Next step

Bring us your teams and your documents

Let us know whether you're a bank, a brokerage, a fintech, or a corporate finance desk, and which groups need this first. We'll turn that into a syllabus and schedule. The depth matches what your desk does.
Contact us
Illustrated figure reviewing a workflow board while a small robot assistant holds up a card

Questions

Frequently asked questions

  • Who ends up in the room for finance AI training?

    Analysts, relationship managers, credit and risk staff, and reporting and operations teams typically train together. Cohorts split by role if a bank, brokerage, or corporate treasury team wants examples matched more tightly to daily work. Mention your mix when we talk, and we'll plan cohorts around it.

  • Is coding knowledge required?

    No coding background required, and no data-science experience either. The one thing that helps is knowing your own workflows, since every exercise is built on documents your teams already handle.

  • How much time, and delivered how?

    Two days, about 13 hours of instruction, onsite or live online, usually split across half-day blocks so reporting deadlines and market hours keep running.

  • Will the syllabus actually match how our institution works?

    It has to, or the exercises won't land. Before the training starts we run a discovery call and look at a handful of anonymized sample documents or workflows from your team. Then we adjust the cases and depth. A bank and a brokerage need one set of examples. A corporate treasury desk needs another.

  • What determines the price?

    Group size, delivery format, how much customization you need, and location all move the number, so we quote per program rather than from a published rate. Outline your teams and what you're trying to fix, and we'll scope it.

  • What do teams walk away with once the sessions end?

    The prompt library, the document templates, and a written 90-day plan for rolling this out, all built during the sessions themselves. We're available afterward for questions. A refresher or a more advanced session is there if you want one later, though nothing about that is automatic.

  • Do we have to use ChatGPT specifically, or can we bring our own tools?

    Training happens on ChatGPT, Microsoft Copilot, and Gemini, since those are what most teams already have access to. The underlying skills carry over to whatever your institution has approved, including an internal assistant if you've built one.

  • Does the training ever touch actual client or financial data?

    Never. The exercise material is invented, and a case modelled on real reporting has been fully anonymized. A whole module is built around keeping client and confidential data out of tools nobody has approved. The decision line is drawn just as hard: AI drafts and analyses, a person reviews, and a decision about credit, an investment, or a client relationship stays with the person accountable for it.