Unify Fragmented Cash Data with ChatGPT for Reliable Forecasts

Build a Clean, Read-Only Data Layer for ChatGPT

The most effective implementations don’t connect ChatGPT directly to every operational system. Instead, they expose a curated, read-only data layer – typically a data warehouse or structured exports – that consolidates bank balances, ERP ledgers, TMS data and forecast files. This layer becomes the single place ChatGPT can query for cash information.

Practically, start by defining a minimal schema: entities, accounts, currencies, value dates, customer/supplier IDs, payment terms, and cash flow categories (e.g. operating, investing, financing). Use regular jobs or APIs to refresh this layer from each source system. Document where each field originates and how it is transformed so finance can validate it.

Once this is in place, connect ChatGPT (via an API or a secure middle layer) to this curated dataset, not to your raw systems. This keeps operational risk low while still allowing the AI to work with near real-time data.

Create Standard Prompt Templates for Cash View and Variance Analysis

To get consistent results from ChatGPT in cash forecasting, standardise the key prompts your team uses. Start with three categories: current position, short-term forecast, and variance explanations. Store these prompts in your internal knowledge base or in the interface you build around ChatGPT so analysts can reuse and adapt them.

Example prompt for a unified cash view:

System role:
You are a treasury analysis assistant. You work only with the structured cash data provided. 
Always show your assumptions and reference entity, account, currency, and value dates.

User:
Using the latest consolidated cash dataset, provide:
1) Today's consolidated cash position by entity, currency, and bank,
2) A 6-week daily cash balance projection at group level,
3) A short explanation of the main drivers of inflows and outflows.
Highlight any data gaps or inconsistencies you detect.

Example prompt for explaining forecast variances:

System role:
You are a finance and treasury expert. Compare forecasted vs. actual cash flows.

User:
Compare the 4-week cash forecast generated on <DATE> with the actuals up to today.
Explain the top 10 variances over €100k by:
- Customer or supplier
- Original due date vs. actual payment date
- Payment terms assumptions vs. reality
Flag structural issues (e.g. systematic late payments) vs. one-offs.

By standardising these prompts, you enable repeatable workflows and make it easier to train the team.

Automate Normalisation of Bank and ERP Formats Before They Reach ChatGPT

One common pitfall is asking ChatGPT to clean up every detail of raw bank and ERP exports. While it can help, it’s more reliable to handle repetitive structural transformations in code and use the AI for interpretation and reconciliation, not for low-level parsing.

Set up simple scripts or ETL processes that convert all bank files (MT940, CAMT, CSV) into a common structure, and map ERP GL accounts to standard cash flow categories. Then provide ChatGPT with a description of that structure and the mapping rules. For example:

System role:
You receive transactions in a unified format with fields like:
entity_id, account_id, value_date, amount, currency,
counterparty_name, counterparty_id, gl_account_group, cashflow_category.
Use gl_account_group and cashflow_category to classify flows.
If a mapping is unclear, ask for clarification instead of guessing.

This approach uses deterministic logic for structure and lets ChatGPT focus on the ambiguous, higher-value parts of the problem.

Use ChatGPT to Generate Scenario-Based Cash Simulations

Once you can reliably query a unified cash view, the next step is to use ChatGPT for scenario analysis. Instead of manually creating separate spreadsheets for “optimistic” and “stress” cases, let the AI apply different assumptions to your base forecast and document the logic behind each scenario.

Example scenario prompt:

System role:
You are a treasury scenario planning assistant. You work with structured cash forecasts.
Always describe the assumptions you apply.

User:
Starting from the current 12-week base cash forecast, create three scenarios:
1) Late customer payments: 30% of receivables from key customers are paid 20 days late.
2) Supplier pressure: 25% of top suppliers shorten payment terms by 10 days.
3) Combined stress: apply both 1) and 2).
For each scenario, show:
- Minimum cash balance per week at group level
- Weeks where available liquidity is negative
- A short explanation of key drivers.
Highlight any covenant risks if available.

This allows decision-makers to quickly see where to adjust funding, collections or spending under different conditions.

Embed ChatGPT Workflows into Existing Treasury Processes

To make AI-enabled cash forecasting stick, integrate ChatGPT into existing routines rather than creating a separate “AI corner”. Identify recurring meetings and reports – weekly cash calls, monthly funding plans, board liquidity updates – and define exactly how ChatGPT will contribute to each.

For example, for a weekly cash call, define a workflow:

• ETL refresh runs at 06:00, updating the consolidated cash dataset.
• A scheduled job triggers ChatGPT with a standard prompt to produce a unified cash report and variance analysis by 07:00.
• The treasury analyst reviews, corrects if needed, and enriches the narrative before the 09:00 meeting.

Document this in a simple playbook with links to the standard prompts and data checks, so the process is robust even if key people are absent.

Track KPIs: Reconciliation Time, Forecast Accuracy and Decision Lead Time

To prove value and continuously improve, define clear KPIs for your ChatGPT cash forecasting solution. Focus on metrics that directly reflect the fragmentation problem: time spent on manual reconciliations, number of forecast versions circulating, and time from data cut-off to an approved forecast.

For example, you might track:

• Manual reconciliation time per cycle: target a 30–50% reduction after the first 3–4 months.
• Forecast accuracy: measure absolute deviation between forecast and actual cash balance at 4 weeks; aim for stepwise improvements as data quality and prompts mature.
• Decision lead time: track how early treasury can identify expected shortfalls and propose actions compared to the pre-AI baseline.

Expected outcomes for a well-implemented set-up: treasury teams can cut reconciliation and report preparation time by 30–60%, shorten forecasting cycles from days to hours, and increase confidence in short- to mid-term cash views, enabling earlier action on funding, collections and spending.