AI Reshapes Collections - Decisimo
AI enables more precise segmentation, which helps teams divide customers more accurately. By grouping customers by payment history, demographics, and other relevant data points, teams can run more targeted collections activity. It is now easier to identify customers most likely to pay.
Sophisticated Segmentation Techniques
Examples of segmentation techniques include:
- Scoring and probability of recovery models
- Machine learning algorithms that analyze customer data
- Decision engines with batch processing capabilities
Benefits of Decision Engines Adoption
Decision engines are a strong choice for segmenting customers with machine learning. Analysts can update decision logic quickly to keep models accurate and effective. By processing data in batches, these platforms can handle large volumes efficiently.
Early adopters of decision engines see clear benefits:
- More accurate customer segmentation
- Better focus on customers most likely to pay
- More consistent optimization of collections strategies over time
Enhanced Collections Efficiency with Voice Bots
AI also improves collections efficiency. Voice bots can handle collections conversations and the first pre-collection calls. As the technology improves, voice bots will handle more complex scenarios with more appropriate responses and alternative resolutions.
Transforming Collections Team Structures
Collections team structures are changing as well. Bots can handle initial soft collections for accounts that are up to 10 days past due. Human agents can then focus on more complex cases where a personal approach matters more.
The Importance of Personalized Approaches
Still, voice bots are not the right fit for every customer. Some cases need a personalized approach. Effective customer segmentation remains important because it helps teams separate customers suited for bots from those who need human intervention.
In summary, AI is changing debt recovery. It supports more accurate customer segmentation, better efficiency, and a better customer experience. As AI continues to develop, the collections industry will likely see more capable solutions that support innovation and growth. Decision engines with batch processing capabilities help collection agencies, consumer lending companies, consumer banks, and BNPL businesses improve results in their debt recovery work.
Frequently asked questions
What past-due cutoff should route an account to a voice bot?
For early-stage collections, start with accounts up to 10 days past due, then treat that cutoff as a pilot rule rather than a universal boundary. Keep the voice bot to routine first contact. Route disputes, hardship signals, unclear account details, and requests for a person to a human agent. Review results by delinquency age and customer segment before widening the cutoff.
Which data should we use to segment customers for collections?
Start with payment history and previous collections outcomes, then add relevant account information such as days past due and balance. Make each segment lead to a specific treatment, such as a call, a voice bot, or agent review. Keep the first set small enough for the team to compare results and spot segments that are too broad to guide action.
How should we test a new collections segment before expanding it?
Compare the new treatment with the current approach on similar accounts, and track recovery, cost per resolved account, kept payment promises, complaints, and requests for an agent. Review results by delinquency age and segment, since an overall improvement can hide a poor outcome for one group. Set the success measures and review period before the pilot starts.
How often should we change AI-based collections decision logic?
Review performance at least monthly as a starting point, and sooner after a material change in portfolio mix or collections results. AI can propose changes to the decision logic, but a collections owner should review the proposed differences and approve them before a new release. Keep the prior version available so the team can compare results and restore it if the change performs poorly.
What should we measure before shifting agent capacity to voice bots?
Track the share of eligible accounts the bot reaches and resolves, along with recovery, repeat contacts, complaints, and transfers to human agents. Check those results against the current approach before changing staffing plans. If transfers or complaints rise in a particular segment, narrow bot use there and keep agent capacity for those customers.
