Description

As an example of a new way to perform a root-cause analysis, here is an example in case of late invoice payments. Late invoice payments can worsen the relationship with your suppliers. That is why it is important to proactively manage problem areas where late payments often occur and to be able to take action.

For this analysis we make use of a rule-based Machine Learning algorithm that is able to predict a specific event (late payment) given a certain probability. Only features with high impact on the outcome are considered.

Benefits

Ourlate payment algorithm provides the following benefits to your company:

• Get insights on problem areas w.r.t. late payments: which data-attributes are related to late payments
• Improve your customer-supplier relationship by tackling problem areas and take action
• Prevent your company or business unit for future late payments by proactively managing your payment process.

Data

For this analysis we make use of invoice data only. Data that our model requires to analyze late payments contain attributes like vendor name, invoice type, amount, payment date, employee, due date etc.

Result

We train our model on your data and extract highly interpretable sentences pointing to certain problem areas. The output is provided through an interactive dashboard and static report containing all results.

• Dashboard (.pbi)
• Report (.pdf)