As a Credit Manager / Analyst, a big part of my role is to conduct monthly or semi-monthly calls with management, explaining the payment status of our customers. It's crucial for management to understand who’s paying on time, who’s late, and why.
In the past, I relied on VBA macros to automate some reporting tasks. While these macros served their purpose, I wanted something more powerful and flexible—something that could not only help me but also anyone else who wants to analyze customer payment behavior efficiently.
Building a Python Tool for Payment Analysis
I started by creating a Python application that can analyze top overdue companies, identifying those who haven’t paid and the reasons behind it. The goal was to provide clear insights in a way that’s both practical and actionable for management.
But I didn’t stop there. I wanted to take it a step further.
Predicting Late Payments with Machine Learning
Complementing the overdue analysis, I built a predictive model to anticipate which customers might delay payments in the future. This is incredibly important—arguably crucial—for management to plan cash flows, allocate resources, and proactively address potential issues.
By combining Python analytics with machine learning predictions, this tool goes beyond reporting: it provides foresight into customer behavior, helping management make more informed decisions.
Check It Out
I’ve shared the full project on GitHub, along with a video where I walk through the tool in great detail, explaining both the code and the thought process behind it:
GitHub Repo: https://github.com/BekBrace/customer-payment-analysis
Video Walkthrough:
So, whether you’re a process lead, team lead, , data enthusiast, or Python developer, I think that this project can show how you can combine analytics and machine learning to turn raw data into actionable insights. Cheers -
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