- Head of Debt Collection Division
- Risk Managers
- Debt Collection Workflow Experts
- Data Administrators
To whom do we recommend?
- Collection agencies, debt buyers, creditors and companies with debt collection and/or factoring “issues”
Aim of the project
Support the process of small and unsecured debt collection with statistics and data mining models.
There are several statistical methods to assess not only the process but also the relevant participants. In statistics, logistic regression uses numerical and/or categorical variables as predictors to forecast the outcome of a given event (will/won’t pay). Decision tree model creates a graphical decision support tool as an output that uses a tree-like graph to display the decisions and the possible chances of event outcomes, resource costs, and utility. The variables of the living area are essential to improve the precision of our models.
It can be used to validate the existing methodology or create a new methodology and a system of recommendations.
DOs and DON’Ts
The output of the decision tree model is a tree-like graph that represents the results of the decision rules given by the model. Through the number and percentage of good and bad cases on a leaf we can evaluate the goodness and the strength of the split.
Why we are different