Payment Pattern
Introduction
Payment Pattern is a machine learning model that summarises user payment behaviour across PAYG customer contracts. It relies on three modules found on the customer page: Credit Score, AI Summary, and Payment Segmentation. The payment pattern is calculated for customers who have had an active installation for at least 90 days (3 months).
Description
A payment pattern has different labels generated using Machine Learning (AI) for every three months that an account is active. These labels are: Monthly Fixed, Monthly Irregular, Slow Pay, Starting Window, Weekly Intermittent, Weekly Ontime, Weekly Slow Pay, Negative and Scaling Artifact.
Monthly Fixed: It means the account is paid consistently on the same day of each month for a similar amount.
Monthly Irregular: The customer pays monthly, but not consistently in terms of timing or amount.
Slow Pay: It means payments are made with no clear pattern and periods of being expired.
Starting Window: It means the account is still in its downpayment period for at least part of the
month.
Weekly Intermittent: Is a pattern where payments are made some weeks but not others, often with late payments and frequent gaps lasting several days to weeks.
Weekly Ontime: Is a pattern with consistent weekly payments, where most payments are made on time and any gaps last only 1 or 2 days.
Weekly Slow Pay: Is a pattern where payments happen weekly but are often late, with long gaps, sometimes over a month, between payments.
Negative: Is a pattern where no payments are made at all during the entire period.
Scaling Artifact
After three months of active installation, the customer's payment pattern is reflected on the account, indicating the category it belongs to.
i.e The image below shows the category of this customer account, including the most recent payment pattern and the pattern history over the past three months.
BBOXX