Recovery intelligence that directs effort at the accounts most likely to pay.
Scoring and prioritisation for a debt recovery operation, so collection effort is aimed at the accounts where it will actually land. The client has asked not to be named, and no performance figures are published.
How it is put together
Trusted by 500+ Clients
What we built.
Debt recovery is a queue problem before it is a collections problem. A recovery team can only make so many contacts in a day, and the order it works the list decides how much of the portfolio is ever recovered. Worked alphabetically or by balance, effort lands on accounts that were never going to pay and misses ones that would have.
Moweb built the intelligence layer that orders that queue: scoring accounts on how likely they are to resolve, and surfacing the ones where contact is worth making now.
This engagement is published without the client's name and without figures. What follows describes the shape of the problem and the approach taken, not measured outcomes.
Key challenges
The constraints below are the ones that shape any recovery scoring system. They are described in general terms because the specifics of this portfolio are confidential.
- 1Recovery capacity is fixed. Every hour spent on an account that will not resolve is an hour not spent on one that would, so the ranking matters more than the volume of contacts.
- 2Historical data reflects historical behaviour. A model trained on which accounts were previously contacted learns the old prioritisation, not the underlying likelihood of recovery.
- 3Collections is regulated. Contact frequency, timing and treatment are constrained, so a score is only useful if it fits inside rules the business already has to follow.
- 4A score nobody trusts gets ignored. Recovery agents need to see why an account is ranked where it is, or they fall back to working the list their own way.
Our solutions
What was built:
- A scoring layer that ranks accounts by likelihood of resolution rather than by balance or age.
- Prioritisation that turns those scores into a worked queue, so the ranking reaches the people making contact rather than sitting in a report.
- Reasoning surfaced alongside each score, so an agent can see what drove it and the ranking is auditable rather than opaque.
More case studies.
Proof
Work we have shipped.
Have a recovery or risk problem worth scoring?
We can talk through what your data supports before anything is built. If the answer is that a simpler rule would do the job, we will say so.