Bank deposit reconciliation win

Industry and problem

I automated bank deposit reconciliation on the client network with a simple chat agent and clear matching rules. A multi-location retailer needed to stop the scramble to tie point-of-sale deposits to the bank. Staff copied exports into a spreadsheet, then called stores when totals did not match.

Constraints

Finance required that all data stay inside the company network. The bank portal allowed only file exports, and the point-of-sale produced batch and settlement reports. I could not change the bank or the point-of-sale, and auditors wanted a clear trail. For your own project, confirm retention, control, and approval rules with your auditor or compliance advisor.

My approach

I set up a small reconciliation service on their file server. It watched two folders where scheduled jobs placed the daily bank statement and the point-of-sale reports. It normalized fields, mapped store codes, and matched deposits by date and amount plus a descriptor, the bank's text label.

Rules covered weekend deposits that posted on Monday and card fees that change net totals. Unmatched items went to an exceptions queue. Every decision and data change wrote to an append-only log, so entries can be added but not edited.

On network chat

I added a private chat agent on the intranet so finance could ask plain questions. A language model, software that processes and generates text, ran locally so no data left the network. Staff asked show unmatched deposits for the last business day or list open exceptions by store. The agent returned approved results with log pointers.

What I learned

Clear keys beat clever models. A stable data structure and careful field cleanup made the agent useful right away. Limiting scope and keeping everything on network built trust.

Checks you can run this week

• Confirm reconciliation, data retention, and privacy requirements with your compliance officer or attorney before you start.
• List your sources and fields, including deposit date, amount, batch or descriptor, and store code.
• Write simple matching rules with a small fee tolerance, and flag any record that misses.
• Send unmatched items to a shared exceptions list with a short reason picker.
• Pilot the last month of data and compare to your current spreadsheet process.
• Create a private intranet chat channel for finance questions and block external connectors.

How I can help

If you want this pattern inside your network, I design and deploy it with Full Conversational AI Agent, Business Automation, and AI Agents and Personas.