Debt Recovery Hub
Welcome to our information hub for teams looking to automate decision tasks in debt recovery for consumer lending.
We focus on data-driven strategies and collections decisioning to manage and recover consumer debt: a decision table uses days past due and a risk band to set treatment and contact channel.
Here, you will find resources and information on automating different parts of the debt recovery process.
Whether you work in consumer lending today or are getting started, this hub gives you practical material to improve and streamline your debt recovery work through automation.
Collections Introduction
- A Practical Guide to Collections Stages in Lending
- Tools and Communication Strategies
- Key Metrics & Collections Efficiency
- Tracking and Managing Promise to Pay
Collection stages
- Segmented pre-collection activity
- What Collections Agents Should Avoid
- Analyzing a Sample Call Script
External recovery
- Outsourcing vs. Insourcing Debt Collection
- Selling Debts in Consumer Finance
- Estimating the Costs of the Collections Process
Decisioning and Analytics
Frequently asked questions
What is collections decisioning in consumer lending?
Collections decisioning in consumer lending uses data-driven strategies to manage and recover consumer debt. A decision table can use days past due and a risk band to set treatment and contact channel. This is one way to automate a debt recovery decision task.
How can a decision table be used in debt recovery?
A decision table can use days past due and a risk band to set the treatment and contact channel for a consumer debt account. In consumer lending, this is an example of automating a debt recovery decision task.
What topics do debt recovery automation resources cover?
Debt recovery automation resources cover collections stages, communication strategies, collections metrics, promise-to-pay tracking, and pre-collection activity. They also cover external recovery, collection costs, artificial intelligence in collections, and champion-challenger testing.