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Automate Bank Reconciliation with AI Agents

Use AI agents to automate bank reconciliation in under 2 minutes — no coding required. Includes free toolkit.

Created byPrashant Panchal, ACA, FinDataPro (Prashant Panchal)

Last updated 6/2026

English

What you'll learn

Automate a complete bank reconciliation in under 2 minutes using AI agents and a Python engine — no coding required
Run live demos on two AI platforms: Claude Cowork and GPT-4o in Antigravity IDE — same data, identical results
Read and interpret a 4-tab audit-ready Excel output: matched transactions, confidence scores, and flagged exceptions
Classify and clear every type of reconciling exception — timing differences, missing entries, errors, and unknowns
Download and run the free AutoBankRec toolkit with your own bank statement and ERP data — Any formats included

Course content

6 sections • 14 lectures • 1h 11m total length

Welcome: What You'll Build in This Course3:58
Intro to the course, the system, and what students will be able to do by the end. Six sections, two live demos, one free toolkit.
Who This Course Is For3:21
Defining the target audience — finance managers, accountants, and bookkeepers at SMEs. GCC context, manual bank rec pain points, and who this is NOT for.
Prerequisites & Two Ways to Run This (Download the Auto Bank Rec Tool Kit here)3:59
The only requirement is an AI agent environment. Covers Path A (Claude Cowork) and Path B (any LLM + IDE). Side-by-side comparison and the key principle: the engine does the work, the LLM is just the interface.

What Is Bank Reconciliation and Why It Matters4:33
Definition, diagram, and the real stakes — fraud detection, audit compliance, cash flow accuracy, and working capital. Frequency table by organisation type.
How Companies Actually Do Bank Rec Today5:28
Four-tier ERP landscape: large enterprise, mid-market, SME in Excel, and outsourced. The typical eight-step manual workflow and where the hours go.
Where ERPs Fall Short: The Four Gaps5:14
Exact-match-only logic, no fuzzy matching, binary output with no confidence scoring, and the SME access gap. What does a better system need to look like?

What Is an Agentic Workflow? (Plain Language)5:10
Clear definition: an agent that receives one instruction, executes multiple steps, and delivers a result without human intervention at every step. AI Assist vs Agentic AI vs RPA — with the GPS vs self-driving car analogy.
Why Bank Reconciliation Is the Perfect Starting Point5:37
Five reasons: structured data, clear pass/fail output, high volume with no creative judgment, audit sensitivity, and the strategic entry point that opens the door to broader workflow automation.

Templates, Engine & Output: How the System Works7:46
Three-component architecture. Bank statement and ERP templates (column maps, row 7 start). engine.py — how fuzzy matching works, confidence scoring examples. Four-tab output report. Data privacy: your financial data never leaves your machine.
Setting Up Your Files: The Five Golden Rules5:10
Do not rename files. Do not change sheet names. No rows above row 7. Same folder as engine.py. Matching periods. Three common mistakes — blank rows, total rows, amounts as text. Pre-run checklist.

Live Demo: Running AutoBankRec in Claude Cowork11:17
Full end-to-end walkthrough. February 2026 demo data — 30 bank transactions, 28 ERP entries, three deliberate challenges. One prompt, engine runs, output generated. Reading all four tabs of the output report.
Live Demo: Same Data, Different LLM (Antigravity + Gemini CLI)3:25
Identical dataset, identical prompt, different AI environment. Gemini CLI in Google Antigravity IDE. Results: 25 matched, 89% match rate — identical to Cowork. Prove the system is vendor-independent.

Handling Exceptions: What Unmatched Items Really Mean3:50
An unmatched item is a question mark, not a red flag. Four exception types: timing difference, missing entry, data entry error, genuine unknown. The right division of labour between engine and human. Six-step workpaper completion checklist.
Course Wrap-Up: What You Now Have2:17
Full recap of all seven sections and what the student is leaving with. The bigger picture — bank rec as the gateway to agentic finance workflows. Thank you.

Requirements

Basic Excel familiarity — you do not need to know how to code
Access to Claude Pro (Claude Cowork) or any IDE (VS Code/Antigravity) LLM such as GPT-4o, Gemini, or Copilot
A bank statement and an ERP bank book export to practise with — demo data is provided if you don't have your own
A Windows or Mac computer with internet access
No accounting software licence required — the engine runs locally on your machine

Description

Every month, the same drill.

Export the bank statement. Open the ERP. Start matching rows — one by one, line by line — for hours. Flag the exceptions. Chase the differences. Pray the numbers reconcile before the CFO asks for the cash position.

Sound familiar?

This course eliminates that workflow entirely.

In under two minutes, an AI agent reads both files, matches every transaction with fuzzy logic, flags every exception, and hands you a clean, audit-ready Excel report — confidence scores included. You watch. You verify. You close faster.

No coding. No cloud uploads. Your financial data never leaves your machine.

WHAT YOU WILL LEARN

- How AI agents work in a real finance workflow — not theory, not hype

- How to run the FinDataPro AutoBankRec engine with a single prompt

- How to read and trust the four-tab output: Summary, Matched, Unmatched Bank, Unmatched ERP

- How fuzzy matching works — and why it catches what exact-match logic misses

- How to run the same workflow on different AI platforms (Claude, GPT-4o, Gemini — any LLM)

TWO COMPLETE LIVE DEMOS

You will watch the entire process from prompt to output twice. First in Claude Cowork, then in Antigravity IDE with GPT-4o. Same data. Same result. Because the intelligence is in the engine, not the AI.

EVERYTHING IS FREE

Free course. Free toolkit. Free templates. Free demo data.

Download the FinDataPro AutoBankRec kit, fill in your data, and you can run your first automated reconciliation before you finish the course.

WHO BUILT THIS

FinDataPro is a finance education and tools platform trusted by professionals across 40+ countries. This toolkit was built by a finance executive with 19+ years in the field — for the people who actually do the close.

If you are still reconciling manually, this course will change your month-end forever.

Enrol free. Start today.

Who this course is for:

Finance professionals, accountants, and contFinance professionals and accountants who perform monthly bank reconciliations manually and want to cut the time from hours to minutesrollers who perform monthly bank reconciliations manually and want to automate the process. GCC, India, and global finance teams welcome.
CFOs, Finance Managers, and Controllers looking to introduce agentic automation into their month-end close process
Finance teams in the GCC, India, and broader MENA region using ERP systems such as SAP, Oracle, QuickBooks, or Tally
Accounting professionals curious about AI agents in finance but with no coding background — this course requires zero programming
Freelance accountants and bookkeepers who handle reconciliations for multiple clients and want to scale their output
Finance students and early-career analysts who want a practical, real-world automation skill to add to their CV

Automate Bank Reconciliation with AI Agents

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Course content

Introduction & Course Overview3 lectures • 11min

The Problem - Why Bank Rec Still Breaks3 lectures • 15min

The Agentic Finance Shift2 lectures • 11min

The FinDataPro AutoBankRec System2 lectures • 13min

Live Demonstrations (Auto Bank Rec in action with Claude Cowork and Any IDE2 lectures • 15min

Conclusion2 lectures • 6min

Requirements

Description

Who this course is for: