Hey guys, hope you're all doing well.
Most programming projects start with something familiar:
"Let's build a to-do app."
I've built projects like that myself. They're useful for learning, but recently I wanted to build something different.
I wanted to take a real business problem and turn it into a Python application.
So I built a Customer Payment Analysis Program for Accounts Receivable (AR).
What is Accounts Receivable?
If you're not familiar with Finance, AR is basically money that customers owe a company.
A company sends an invoice, the customer has a payment due date, and until the money arrives, that invoice remains outstanding.
When you have hundreds or thousands of invoices, some useful questions appear:
- Which customers are paying late?
- How much money is overdue?
- Which customers consistently pay late?
- What does their payment behavior look like?
This is where Python can help.
The idea
The application takes customer payment information and analyzes it to produce useful metrics.
For example:
Customer: ABC Corporation
Invoices: 24
Total invoiced: €248,500
Outstanding: €27,100
Average days late: 12
Late payments: 8
Instead of manually going through rows in a spreadsheet, we can let Python do the repetitive work.
Using Pandas
For this type of data analysis, Pandas is a natural choice.
A simplified example looks like this:
import pandas as pd
df = pd.read_csv("payments.csv")
summary = (
df.groupby("Customer")
.agg(
invoices=("Invoice", "count"),
total_amount=("Amount", "sum"),
average_days_late=("DaysLate", "mean")
)
.reset_index()
)
print(summary)
A few lines of Python can turn raw payment data into a useful customer summary.
Looking at payment behavior
The interesting part isn't just calculating totals.
It's identifying patterns.
For example:
Customer A → 2 days late on average
Customer B → 12 days late
Customer C → 43 days late
We could then categorize customers according to their payment behavior:
0–5 days Excellent
6–15 days Good
16–30 days Attention
31–60 days High Risk
60+ days Critical
These categories are only an example, of course. A real company would use its own credit policies and business rules.
Why I built it
This project reminded me why I enjoy programming.
A payment isn't just a number in a spreadsheet.
An invoice isn't just another row.
Behind that data is a real business process.
Programming allows us to take that process, understand it, and turn repetitive work into something automated.
And you don't need to be a Finance expert to apply the same idea.
The exact same approach could be used for:
- Sales data
- Website analytics
- Server logs
- Inventory
- Customer activity
- IoT data
The basic idea is always similar:
Input → Process → Analyze → Output
What's next?
There are plenty of ways I could extend this project:
- Add charts and dashboards
- Generate Excel or PDF reports
- Add historical payment trends
- Build a web interface
- Add more advanced customer scoring
Eventually, the Python logic could become the backend of a proper application using something like FastAPI.
Watch the project
I also made a YouTube video showing the project and the development process:
Final thoughts
I think some of the best programming projects come from problems we actually encounter in everyday work.
You don't always need to build another to-do application.
Look at the work around you.
Maybe there's a spreadsheet, report, calculation, or repetitive process that could become a program.
That's exactly what I wanted to explore with this project: taking a real-world Accounts Receivable problem and turning it into Python.
p.s. This article is modified with AI for grammatical polishing
