AI for Finance
Invoice entry and payment matching, done automatically.
What is AI finance?
AI for finance reads incoming invoices and receipts, extracts the figures into your accounting system, matches payments against what is owed including mobile money, and holds anything it is unsure about for a person rather than guessing.
Finance teams lose days each month to two mechanical tasks: typing invoice details into the accounting system, and matching payments to the invoices they settle.
Both are slow, both introduce errors that are hard to find later, and neither requires the professional judgement your finance team is actually employed for.
M-Pesa is where this pays most in Kenya. Reconciling mobile money receipts against invoices by hand consumes a large share of month end, and the matching rules are consistent enough to automate reliably.
Why we hold items rather than force a match
Duara builds finance automation from Nairobi, largely for businesses where month end has become a three day exercise involving people who should be doing something else.
We set confidence thresholds conservatively and hold unmatched items for review rather than forcing a match. A wrong reconciliation is considerably more expensive to unpick than an unmatched item is to clear, and clients who ask us to loosen the thresholds are usually asking for the wrong thing.
Who this is for
- Month end takes days rather than hours
- Somebody types invoice details by hand
- Matching M-Pesa payments to invoices is manual
- Errors surface weeks after they were made
Industries we serve
- Distribution and wholesale
- Retail chains
- Construction
- Professional services
- Healthcare groups
- Manufacturing
Problems this solves
These four conditions usually appear together, and together they define a finance function spending its time on data entry.
- Invoice details typed by hand. Somebody reads each invoice and keys the figures into the accounting system, which is slow and where most errors originate.
- M-Pesa reconciliation done manually. Mobile money receipts are matched against invoices by eye, which is the single largest manual task in many Kenyan finance teams.
- Month end consuming days. The close takes three days because everything mechanical has been left to accumulate rather than being handled as it arrived.
- Errors found long after the fact. A misposted figure surfaces at year end, by which point tracing and correcting it costs far more than catching it would have.
What we deliver
Invoices read and posted automatically, payments matched including mobile money, unmatched and low confidence items held for review, and a clear record of what was matched under which rule.
Invoices read and entered from email or photographs
Payments matched against invoices, including M-Pesa
Unusual and low confidence items flagged for review
A clear record of what was matched and by which rule
Integration with your existing accounting software
Month end reporting assembled without manual exports
Features
- Reading from photographs. Invoices arriving as phone photos are handled, which matters because that is how many suppliers actually send them.
- Mobile money matching. M-Pesa references are matched against invoices using rules built from your own payment history.
- Conservative confidence thresholds. Anything below the threshold is held rather than matched, because unpicking a wrong match costs more than clearing a queue.
- Rule transparency. Every automated match records which rule produced it, so a disputed entry can be traced immediately.
- Approval retained. Posting can require sign-off, and does by default, so the finance team keeps control of the ledger.
Benefits
- Month end shrinks to hours. Work handled as it arrives rather than accumulating removes most of the close itself.
- Fewer errors reaching the ledger. Reading rather than retyping removes the most common source of posting mistakes.
- Reconciliation stops being a job. Mobile money matching runs continuously instead of consuming days at the end of the period.
- Finance staff do finance work. The team spends its time on judgement and analysis rather than on data entry.
Technologies we use
- Document reading. Extraction from PDFs, scans and phone photographs, tuned to the invoice formats your suppliers actually use.
- Accounting integration. Direct connection to QuickBooks, Xero, Sage or Zoho Books so entries post where they belong.
- M-Pesa reconciliation. Matching mobile money statements against outstanding invoices using reference and amount rules.
- Confidence scoring. Every extraction and match carries a confidence level, with thresholds you can set.
- Exception queueing. Low confidence items routed to a review queue rather than posted or discarded.
Accuracy thresholds matter more than extraction cleverness. A system that is right ninety five percent of the time and honest about the rest beats one that guesses confidently.
How the process works, step by step
Finance automation usually runs alongside manual processing for a full month before anybody relies on it.
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Step 1
Free volume review
We count invoices and payments per month and establish what the manual process currently costs in hours.
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Step 2
Format training
We tune extraction against your actual supplier invoices rather than generic templates.
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Step 3
Parallel running
The system processes alongside your team for a full cycle so accuracy is measured rather than assumed.
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Step 4
Threshold setting
Confidence thresholds are set from the parallel run, with your finance lead deciding where the line sits.
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Step 5
Cutover with review queue
Automatic posting begins, with everything below threshold held for a person.
Quality assurance and how we work
The commitments that apply when automation touches your ledger.
- Uncertain items are held, never guessed. Anything below the confidence threshold goes to a review queue. Unpicking a wrong match costs far more than clearing a queue.
- We run parallel for a full cycle. Accuracy is measured against your team output for a complete month before anybody relies on the system.
- Every match records its rule. A disputed entry can be traced to the rule that produced it immediately, without investigation.
- Your finance lead sets the thresholds. Where the confidence line sits is their decision, not ours, because they carry the consequence.
- Approval before posting stays available. It is on by default and we recommend keeping it for at least the first quarter.
Frequently asked questions about AI finance
Does it work with our accounting software?
Yes, with the common ones including QuickBooks, Xero, Sage and Zoho Books. Others usually connect through their API.
What if it matches something wrongly?
Matches below the confidence threshold are held rather than posted. Unpicking a wrong match costs far more than clearing a review queue.
How reliable is M-Pesa matching?
Good where payment references are used consistently. Where they are not, more items land in the review queue, and we tell you that during the parallel run.
Can it read invoices sent as photographs?
Yes, which matters because that is how many suppliers actually send them. Poor quality images are flagged rather than guessed at.
Do we have to approve postings?
By default yes, and we recommend keeping approval for at least the first quarter before relaxing it.
Why run parallel for a whole month?
Because accuracy has to be measured against your team output rather than assumed. One cycle covers the full range of invoice types.
Who decides the confidence thresholds?
Your finance lead, because they carry the consequence of a wrong match. We recommend a starting point and they adjust it.
What happens to items it cannot match?
They go to a review queue with the reason shown. Nothing is discarded and nothing is forced through.
Can it handle multiple currencies?
Yes, where your accounting system supports them, with exchange handling following your existing policy.
Does it do three way matching?
Where you hold purchase orders and delivery notes in a connected system, yes. Without those it matches invoice to payment only.
How long before finance automation runs?
Around six weeks including the parallel month, which is the part that cannot sensibly be shortened.
Will it reduce our finance headcount?
Most clients redeploy rather than reduce. The work removed is data entry, and the work remaining needs the people you already have.
What about supplier invoices in different formats?
Extraction is tuned against your actual suppliers rather than generic templates, which is why format training is a distinct step.
Can it flag duplicate invoices?
Yes, and duplicates are one of the more valuable catches, because paying twice is both common and rarely noticed.
Does it detect fraud?
It flags unusual patterns for a person to examine. It does not conclude that something is fraudulent, because that judgement has consequences.
Is our financial data secure?
It stays within your systems, and is never used to train public AI models. Access is limited to what the task requires.
What if a supplier changes their invoice layout?
Extraction confidence drops and items go to review, which alerts you rather than posting wrong figures silently.
Can it chase unpaid invoices?
That sits with the finance agent rather than here. The two work together but are scoped separately.
Does it help with VAT?
It prepares the underlying figures accurately. Filing and the final check remain with your accountant.
What does finance automation cost monthly?
Invoice and transaction volume determines it. The monthly figure is projected from your own counts before you commit.
Glossary of terms
Finance automation vocabulary, defined plainly.
- Reconciliation
- Matching payments received against the invoices they settle so the ledger reflects reality.
- Confidence threshold
- The certainty level below which the system holds an item for review instead of processing it.
- Exception queue
- The list of items the system could not handle confidently, waiting for a person.
- Extraction
- Reading figures and details off a document so they can be entered without typing.
- Posting
- Writing an entry into the accounting ledger, after which correcting it requires a journal.
- Three way match
- Checking an invoice against the purchase order and the delivery note before paying it.
Related services in AI Implementation
Talk to us about AI finance
Count how many invoices you process monthly and how long month end takes. Those two numbers usually decide this on their own.
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