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AI training for treasury: from sources to a concise brief
Short answer
AI training for a treasury team teaches you to write a morning brief in which the reader sees at once the publication date, the period of the data and the source of the commentary. The key exercise is catching a draft that describes last week's data as today's situation. At Syntalith team training costs from €1,150 net a day. With 5 people each saving 1.5 hours a week, it pays back in 1.1 months after licence costs.
- net per day of closed training for the team
- from €1,150
- people in one training group
- up to 15
- after this time, we measure the effect on the same tasks
- 30 days
We belong to the OpenAI and Anthropic partner programmes and learn through their official courses. What this means ↓
Author: Syntalith · 3 min read
Statistics
Finance and AI: statistics 2026
1.1 mo.
payback of a €1,150 training at 5 people saving 1.5 hours a week each: €1,133 of time regained, €89 of licences a month. Scenario.
40%
According to a study published in Science (2023), ChatGPT cut the time 453 professionals spent on writing tasks by 40%.
Source: Noy and Zhang, Science (2023) ↗
25.1%
According to a Harvard Business School study (2023) of 758 consultants, tasks within the model's capabilities were done 25.1% faster with AI.
80%
According to Anthropic (2025), Claude cut the time for financial analyst tasks such as interpreting financial data by about 80% in the conversations it analysed.
Source: Anthropic, Estimating AI productivity gains (2025) ↗
15.1%
According to Eurostat (2025), 15.1% of people aged 16 to 74 in the EU used generative AI tools for work.
Figures checked: 22 October 2026
A new publication with last week's data
An analyst prepares the morning brief from material published today, but with observations from the previous working week. The draft prepared with AI calls them „today's market situation”. The reader will assume the brief describes the current day, although the author has only older data, and a link to the new publication does not explain the difference.
The corrected brief gives the publication date and the observation period separately, keeps the link and notes that current data is missing to describe today. The head of the department gets a summary of the material available and a specific question:
Which approved source will let us add observations from today to the brief?
In the exercise we use public publications. In the API documentation of the Polish central bank, NBP, the publication date of a table is kept apart from the trading date given for table C, and the documentation also distinguishes the type and number of a table. That is good material for practising how to read dates when a description gets shortened.
- 01Questionwhat the reader of the brief wants to know
- 02Sourcethe primary publication with its definition and data period
- 03Draftan AI brief checked for its dates
- 04Commentaryinformation from the source kept apart from the author's opinion
- 05Trialupdating the brief after the source is corrected
- The publication date and the observation period given separately
- A link to the source beside every figure
- The author's commentary marked as commentary
- Missing data named plainly, with a question about the source
What it costs and when it pays back
- Time saved
- 5 people × 1.5 h a week × 4.33 = 32.5 h
- Value of that time
- €1,133
- Licences
- $100 (€89)
- Left after licences
- €1,044 a month
Payback of a €1,150 training
1.1 months
The hours saved are the scenario's assumption, which we check by timing the tasks before the training and after 30 days. €34.90 an hour is the EU average hourly labour cost for 2025 according to Eurostat. Claude Team licences at $20 per person a month, at the ECB rate of 1.1205 USD per euro on 22 October 2026.
We measure the hours in the scenario before the training and after 30 days, on the same team tasks. If after a month checking AI texts takes as long as writing from scratch, we change the exercises or the scope of the tool.
According to a study in Science (2023), ChatGPT cut the time 453 professionals spent on writing tasks by 40%. In treasury briefs that gain only counts when the draft keeps the dates and sources, so we also measure the corrections of such sentences.
First check where the time goes
If most of the time goes on pulling the same data again and again, first check your current data service or spreadsheet. Training makes sense when the team has the data but its briefs come back with questions about dates and sources. Combining data from systems into a fixed report is a separate scope, described in the article on financial reporting for the CFO.
Time goes on pulling fixed data
a data service or a spreadsheet
no build
Briefs come back with questions about dates and sources
training on writing briefs with AI
from €1,150 a day
A fixed report from several systems
reporting automation
from €3,000
Where to start
We set the programme on a brief whose reader had to ask what the data meant. The analyst brings knowledge of the sources, and the head of the department says which questions the brief has to answer. We run the workshop as part of team training. For the free analysis, a description of one task the team does every week and of the tools it uses is enough. You get the written training programme within 2 working days.
Worked example · Scenario
How we count the payback: a finance team
We run the same count for your team in the free analysis.
€2,094
a month for preparing 20 reports
Payback: 1.7 mo.
3 hours to collect the data and prepare a report and checking in 15% of the old time are our assumptions. €34.90 an hour is the EU average hourly labour cost for 2025 according to Eurostat, for companies with 10 or more employees. Model cost: checking the commentary in our reporting system (about €0.0002) plus compiling the data (Claude Sonnet 5.5, about 30,000 + 2,000 tokens), at the ECB rate of 1.1205 USD per euro on 22 October 2026.
See the full calculation →
