Finances

Automatic matching of payment transactions to points of sale

Thousands of bank transactions every day, hundreds of points of sale — and zero manual matching. The system automatically links transactions from customers' bank accounts to specific partner stores, using transaction data, merchant identifiers and geolocation.

95%
transactions matched automatically
<2s
time to match a single transaction
10x
more transactions processed than manually
Free Consultation
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Industry
Finance & Accounting
Department
Finances
Implementation time
10-14 weeks
Scale
5000+ transactions per day, 500+ points of sale, 50+ operational zones

Manual matching of bank transactions to hundreds of points of sale

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A loyalty platform serving hundreds of points of sale across dozens of operational zones had to match thousands of customer bank transactions every day to specific partner stores. Data from banks arrived in different formats: merchant identifiers did not always correspond to the store name.

Manual matching required a team of operators comparing transaction data with the points-of-sale database. With 5000+ transactions per day, errors were inevitable. An incorrectly matched transaction meant incorrectly calculated rewards.

New retail partners had to be mapped manually. The lack of a central mapping register meant that knowledge remained in the heads of operators. When a store changed its name or terminal, transactions ended up in the unrecognised queue.

Intelligent transaction matching engine with multi-level mapping

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The implemented matching engine automatically analyses each incoming bank transaction and matches it to a point of sale. The algorithm uses multiple signals at the same time: merchant identifier, name, terminal location, amount and time.

A multi-level location hierarchy enables precise mapping: the transaction is matched to the point of sale, retail chain and operational zone. The system supports aliases and different identifiers across different banks.

The monitoring dashboard shows matching statistics in real time. Unrecognised transactions are routed to a priority queue with suggestions. The system generates alerts when the matching rate drops.

Before and after implementation

Concrete automation results

Transactions matched automatically
Before
0% (everything manual)
After
95%
new feature
Transaction matching time
Before
2-5 min per transaction
After
<2 seconds
-99%
Matching errors
Before
8-12%
After
<1%
-92%
Time to map a new partner
Before
2-4 hours
After
15 minutes
-90%
Unrecognised queue
Before
500-2000 items
After
<50
-97%
Reward calculation delay
Before
1-3 days
After
<1 hour
-95%

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Book a free consultation
Previously, our team spent hours every day manually linking bank transactions to stores. Now the system recognises the merchant, matches it to our database and calculates benefits. We only intervene in ambiguous cases.

Loyalty platform, 500+ retail partners

Our solutions

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Ready to speed up your business?

Schedule a free consultation with our expert and find out in less than 30 minutes:

Which processes in your company are worth automating in the first place - and why they
What AI technologies and tools will work well in your industry
What savings and efficiency growth can you achieve - preliminary calculation of ROI
Answers to your technical and business questions - no jargon, no obligations

What do customers say about us?

They had all the necessary people and tools to get this job done.


CTO, The Codest

The personal skills of the programmers and the team manager were important for this project.


CEO, Bravedigital

The team is not afraid of challenges.


E-Commerce Manager, Kontigo

This is a partner you can trust.


Director, Compensa Vienna Insurance Group

We strongly recommend them for collaboration on any IT challenges.


Head of Delivery, itCraft

The flexibility of the team and the way they are open for feedback is impressive.


Digital Lead, Trooper
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