AI for finance reconciliation
Matching bank statements to invoices: rules first, AI for the rest, people decide.
What this course covers
For finance and accounting staff who match bank statements to invoices and the ledger every month. Why exact matching fails (transfer fees, partial payments, payment gateway and marketplace settlements, dates, mistyped references), writing matching rules in SQL, and cleaning bank exports. Then using AI for similar names, transfer receipts and remittance emails, with tested confidence scores, review queues and an audit trail. The final decision stays with a person.
New to AI? Start with AI & data fundamentals.
Part of the career paths:Data engineer
What you will be able to do
- Break the difference between a bank statement and your records into items that can be explained
- Write matching rules with amount, tolerance and a date window in SQL
- Use AI to suggest matches behind a confidence threshold and a review queue
- Design a reconciliation pipeline with an audit trail an auditor can check
Units and bites
- Unit 15 bitesAbout 15 min
Reconciliation basics
Matching two records of the same money, from a simple table to a pipeline with AI and human review.
- 3 min
What reconciliation is
Two records of the same money, compared line by line until every difference has an explanation.
- 3 min
Why exact matching fails
Fees, partial payments, batched settlements, dates and mistyped references.
- 3 min
Rule-based matching
Amount, date window and tolerance: writing matching rules as a JOIN in SQL.
- 3 min
Where AI helps, and where it must not decide
AI suggests matches with a confidence; people decide, and every decision is recorded.
- 3 min
A reconciliation pipeline
From bank export to report: rules first, AI for the rest, people for the doubtful cases.
- Unit 24 bitesAbout 12 min
Cleaning bank exports
From a messy export file to a table you can match: formats, dates, amounts and references.
- 3 min
Bank export formats
CSV, Excel, PDF and MT940: what they contain and what often goes missing.
- 3 min
Normalising amounts and dates
Thousands separators, debit/credit columns, posting vs transaction dates, and time zones.
- 3 min
Extracting payment references
Pulling invoice numbers out of free-text descriptions, mistyped ones included.
- 3 min
Payment gateway and marketplace settlements
Splitting one transfer into orders, fees, refunds and deductions.
- Unit 34 bitesAbout 12 min
Fuzzy matching and confidence scores
Matching what is nearly the same, and knowing how far to trust the result.
- 3 min
Matching similar names
Abbreviations, typos, and account names that differ from the customer name.
- 3 min
One payment, many invoices
Combined payments and instalments: finding the combination that adds up.
- 3 min
Reading remittances with AI
Extracting invoice numbers and amounts from PDFs and emails, and checking the result.
- 3 min
Confidence scores and calibration
Testing whether "90% sure" is really right 90% of the time.
- Unit 44 bitesAbout 12 min
Exceptions and review queues
What happens to the lines that do not match: who checks them, within what limits, and what is learnt.
- 3 min
Types of exception
Unpaid, in transit, misdirected, duplicated and suspicious.
- 3 min
Designing a review queue
Priority order, the evidence shown, and time limits for resolution.
- 3 min
Tolerances and write-offs
How large a difference is acceptable, and who may approve it.
- 3 min
Learning from reviewer decisions
Turning people's decisions into new rules and evaluation data.
- Unit 54 bitesAbout 12 min
Controls, audit and month-end close
Making AI-assisted reconciliation accountable: audit trail, segregation of duties, personal data and the close.
- 3 min
An audit trail for AI matches
What to record for every suggestion and decision, and how long to keep it.
- 3 min
Segregation of duties
The person who matches is not the one who approves, even when one of them is an AI.
- 3 min
Finance data and privacy
Sending bank statements to an AI service: what is allowed, and what the PDP Law covers.
- 3 min
Month-end close with AI
The close checklist, commentary on differences, and what people still do.
Questions about this course
Learning
How long does AI for finance reconciliation take?
About 63 minutes in all: 5 units made of 21 bites of about three minutes each, and every bite ends with a quick check. You can do one bite a day or several in a row.
Do I need to know how to code?
Not to start. The foundations explain every idea in plain words first; where a course uses SQL or Python, it teaches it from the beginning, with a playground in the lesson.
Where should I start?
If you are new to AI, begin with AI & data fundamentals. If you know the job you want, follow its career path; AI for finance reconciliation has a place in it.
When can I start learning?
Datamew is coming to iOS and Android soon. There is no release date or price announced yet: join the waitlist and we will tell you when the app is out.
Is it available in Bahasa Indonesia?
Yes. Datamew is written in English and Bahasa Indonesia.
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