How to Use AI for Cash Flow Reconciliation Without Exposing Sensitive Data

Your cash flow forecast lives in one system. Your bank statements live in another.  Reconciling them is still one of the most manual, time-consuming tasks in treasury, even in 2026. Amounts don’t align perfectly. Descriptions vary by bank. FX, fees, timing differences break exact matching.

So teams end up with hundreds of rules to maintain in TMS, parallel Excel files, and a lot of manual judgment.

AI can help. But treasury professionals are rightfully cautious. How do you use AI without sending IBANs, account numbers, and counterparty names to external services?

AI Matches Patterns, Not Identitie. An AI model does not need to know who paid you. It needs to understand how transactions behave.

Here’s a practical approach you can implement today, whether you build it yourself or use a tool like TreasuryOS.

The Core Principle: Mask Identifiers, Keep Patterns

AI doesn’t need your actual IBAN to match a payment. It needs:

• Amount
• Date
• Transaction type pattern

What you mask before sending to AI:

What stays intact: amounts, dates, currency, transaction category. These are general and do no have an impact.

DIY Method: Excel + Any AI Chatbot

You can test this approach today with nothing more than Excel and a generic AI tool. Here’s how to do it yourself:

Step 1: Prepare your data in Excel

Create two tabs:

• Forecasts: Date, Amount, Currency, Category, Description
• Actuals: Date, Amount, Currency, Description (from bank statement)

Step 2: Sanitize before export

In a new column, use formulas to mask sensitive data:

=SUBSTITUTE(SUBSTITUTE(A2,"RO49RNCB0082044444440001","[IBAN]"),"Vodafone Romania SRL","[VENDOR]")

Or do a manual find-and-replace for your known counterparties and account numbers.

Step 3: Export to CSV

Export only the sanitized columns. Verify no real IBANs or company names remain.

Step 4: Upload to AI

Use any AI assistant. Suggestion prompt:

I have two datasets: cash flow forecasts and bank statement actuals. 
Match them based on amount, date proximity, and transaction pattern.
For each match, provide:
- Confidence score (0-100%)
- Reasoning
- Flag mismatches that need manual review

[Paste sanitized data]

Step 5: Review and approve

AI will suggest matches. You verify and reconcile in your source system.

What TreasuryOS Does Differently

The DIY method works but has friction: manual sanitization, copy-paste, no audit trail.

TreasuryOS automates this:

1. Auto-masking: IBANs, counterparties (from your master data), references, all replaced automatically before AI sees them
2. Confidence scoring: Each match shows 0-100% confidence with explanation
3. Manual override: Accept, reject, or manually match any item
4. Audit trail: Every AI suggestion and your decision is logged

The Bottom Line

You can use AI for reconciliation today without exposing sensitive financial data. The key is systematic masking of identifiers while preserving the patterns AI needs for matching.

Start with the Excel method to test the concept. When you’re ready to eliminate the manual steps, TreasuryOS handles the sanitization and matching automatically.

Questions? Reply in comments or try TreasuryOS free.