AI Training · Finance
AI Training for Finance
Credit memos, board packs, market notes, contracts, and client letters: a finance team's real output is almost all text, and text is exactly what a language model drafts and reads fastest.
This program turns that speed into a safe habit rather than a shortcut. A person still checks every number before anything goes out the door.
- modules
- 6
- hours
- 13
Some of the 500+ brands we've worked with
Selected brands we’ve worked with across industries.
OdeabankGlobalCase Study
GeneraliGlobalCase Study
Albaraka TürkGlobalCase Study
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.
High volume, repeating structure, and low tolerance for a wrong number make finance a strong match for this tool — and exactly why using it untrained is risky, since a model doesn't know which number in front of it is real. The two-day program pairs drafting and extraction practice in ChatGPT, Copilot, and Gemini with a verify-against-source habit and the GDPR/KVKK rules for what can enter an AI tool; credit and investment decisions, and client-relationship calls, always stay with the accountable person. A discovery call reshapes this draft syllabus around your institution's documents; see the rest in the industry programme list.
The reporting calendar owns the week before the analysis does
Credit memos and board packs eat more hours than the analysis behind them — a model drafts a workable version before the coffee's cold, reclaiming up to 40% of that time per industry pilots, though someone still stands behind what it says.
A contract's forty pages, read end to end, every time
Contracts and filings repeat the same structure page after page — a model pulls dates and terms out in seconds, but someone still checks each against the source, since one missed term costs more than the hours saved.
The same client answer, retyped from memory five times a day
Relationship managers field near-identical questions all day, and wording drifts each time someone rewrites from scratch — AI keeps tone and facts consistent, but deciding what goes to that client stays with the account holder.
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 — 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 staff — buying another tool won't teach anyone which draft to trust on sight or which needs a rewrite; only practice does.
Syllabus
Training syllabus
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
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
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
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
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
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
Keep exploring
Related programs
Next step
Bring us your teams and your documents
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.