Optimising debt collection efforts for over 1 million cases
with the implementation of a cloud-based debt prioritisation solution
“ The solution helped us to work the right cases first and reduce the time taken to work a case by more than 50% ”
Head of Debtors Shared Services - Quote is paraphrased
Case Study
Industry
Health Care
Company Size
10,001+ employees
Problem Space
Predictive Analytics
Azure
Insights
Machine Learning
Debt Collection
Technology Used
Python
AzureSQL
AzureVNet
AzureDataFactory
AzureMachineLearning
Business Context
Thousands of medical cases are processed by large healthcare providers each day. The majority of these cases need to be settled through medical aids or privately by the patients themselves. Often cases can take up to 3 years to finalise. This is an area that had significant potential to improve through data-driven solutions and automation.
Challenge
Improve debt collection efficiency on thousands of bills not paid each day
- The current process prioritises collection efforts based on value outstanding only.
- High number of physical and digital touch points involved in the billing process resulting in errors and omissions costing millions.
Solution
An Azure-based debt prioritisation solution to process thousands of cases each day
- Implemented a data consolidation strategy to create visibility of cases and actions taken across multiple source systems.
- Created a PowerBI dashboard to prioritise and manage debt collection efforts on a daily basis.
Automated Debt Prioritisation Solution
Reduced time to action outstanding cases and improved collection revenue
💰
Over 1 million cases prioritised to approximately 10k with the highest likelihood of collection
🕑
~50% reduction in time taken to work a prioritised case
🚀
Increased visibility of cases across debt, medical and finance systems